{"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":3920517,"sourceId":6960620,"sourceType":"datasetVersion"}],"dockerImageVersionId":30579,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport time\nimport matplotlib.pyplot as plt\nimport seaborn as sns","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2023-11-15T09:52:27.782029Z","iopub.execute_input":"2023-11-15T09:52:27.783130Z","iopub.status.idle":"2023-11-15T09:52:28.990416Z","shell.execute_reply.started":"2023-11-15T09:52:27.783085Z","shell.execute_reply":"2023-11-15T09:52:28.989464Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.feature_selection import SelectKBest, chi2\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Conv1D, MaxPooling1D, Flatten, LSTM, GRU\nfrom tensorflow.keras.optimizers import Adam\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom tensorflow.keras.utils import plot_model","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:52:28.992629Z","iopub.execute_input":"2023-11-15T09:52:28.993667Z","iopub.status.idle":"2023-11-15T09:52:39.975216Z","shell.execute_reply.started":"2023-11-15T09:52:28.993630Z","shell.execute_reply":"2023-11-15T09:52:39.974213Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/edgedata/Edge-IIoTset_99.csv', low_memory = False)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:52:39.976418Z","iopub.execute_input":"2023-11-15T09:52:39.977111Z","iopub.status.idle":"2023-11-15T09:53:20.624885Z","shell.execute_reply.started":"2023-11-15T09:52:39.977075Z","shell.execute_reply":"2023-11-15T09:53:20.623985Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:20.626049Z","iopub.execute_input":"2023-11-15T09:53:20.626343Z","iopub.status.idle":"2023-11-15T09:53:20.655809Z","shell.execute_reply.started":"2023-11-15T09:53:20.626318Z","shell.execute_reply":"2023-11-15T09:53:20.654915Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1945449 entries, 0 to 1945448\nData columns (total 99 columns):\n #   Column                     Dtype  \n---  ------                     -----  \n 0   arp.opcode                 float64\n 1   arp.hw.size                float64\n 2   icmp.checksum              float64\n 3   icmp.seq_le                float64\n 4   icmp.transmit_timestamp    float64\n 5   http.content_length        float64\n 6   http.response              float64\n 7   tcp.ack                    float64\n 8   tcp.ack_raw                float64\n 9   tcp.checksum               float64\n 10  tcp.connection.fin         float64\n 11  tcp.connection.rst         float64\n 12  tcp.connection.syn         float64\n 13  tcp.connection.synack      float64\n 14  tcp.dstport                float64\n 15  tcp.flags                  float64\n 16  tcp.flags.ack              float64\n 17  tcp.len                    float64\n 18  tcp.seq                    float64\n 19  udp.port                   float64\n 20  udp.stream                 float64\n 21  udp.time_delta             float64\n 22  dns.qry.name               float64\n 23  dns.qry.qu                 float64\n 24  dns.retransmission         float64\n 25  dns.retransmit_request     float64\n 26  dns.retransmit_request_in  float64\n 27  mqtt.conflag.cleansess     float64\n 28  mqtt.conflags              float64\n 29  mqtt.hdrflags              float64\n 30  mqtt.len                   float64\n 31  mqtt.msgtype               float64\n 32  mqtt.proto_len             float64\n 33  mqtt.topic_len             float64\n 34  mbtcp.len                  float64\n 35  mbtcp.trans_id             float64\n 36  mbtcp.unit_id              float64\n 37  Attack_label               int64  \n 38  Attack_type                object \n 39  http1_encoded              int64  \n 40  http2_encoded              int64  \n 41  http3_encoded              int64  \n 42  dns_encoded                int64  \n 43  mqtt1_encoded              int64  \n 44  mqtt2_encoded              int64  \n 45  mqtt3_encoded              int64  \n 46  http1_0                    float64\n 47  http1_1                    float64\n 48  http1_2                    float64\n 49  http1_3                    float64\n 50  http1_4                    float64\n 51  http1_5                    float64\n 52  http1_6                    float64\n 53  http1_7                    float64\n 54  http1_8                    float64\n 55  http2_0                    float64\n 56  http2_1                    float64\n 57  http2_2                    float64\n 58  http2_3                    float64\n 59  http2_4                    float64\n 60  http3_0                    float64\n 61  http3_1                    float64\n 62  http3_2                    float64\n 63  http3_3                    float64\n 64  http3_4                    float64\n 65  http3_5                    float64\n 66  http3_6                    float64\n 67  http3_7                    float64\n 68  http3_8                    float64\n 69  http3_9                    float64\n 70  http3_10                   float64\n 71  http3_11                   float64\n 72  http3_12                   float64\n 73  dns_0                      float64\n 74  dns_1                      float64\n 75  dns_2                      float64\n 76  dns_3                      float64\n 77  dns_4                      float64\n 78  dns_5                      float64\n 79  dns_6                      float64\n 80  dns_7                      float64\n 81  dns_8                      float64\n 82  dns_9                      float64\n 83  mqtt1_0                    float64\n 84  mqtt1_1                    float64\n 85  mqtt1_2                    float64\n 86  mqtt1_3                    float64\n 87  mqtt1_4                    float64\n 88  mqtt1_5                    float64\n 89  mqtt1_6                    float64\n 90  mqtt1_7                    float64\n 91  mqtt1_8                    float64\n 92  mqtt1_9                    float64\n 93  mqtt1_10                   float64\n 94  mqtt1_11                   float64\n 95  mqtt1_12                   float64\n 96  mqtt2_0                    float64\n 97  mqtt3_0                    float64\n 98  mqtt3_2                    float64\ndtypes: float64(90), int64(8), object(1)\nmemory usage: 1.4+ GB\n","output_type":"stream"}]},{"cell_type":"code","source":"df['Attack_type'] = df['Attack_type'].str.replace('DDoS_TCP', 'DDoS')\ndf['Attack_type'] = df['Attack_type'].str.replace('DDoS_UDP', 'DDoS')\ndf['Attack_type'] = df['Attack_type'].str.replace('DDoS_HTTP', 'DDoS')\ndf['Attack_type'] = df['Attack_type'].str.replace('DDoS_ICMP', 'DDoS')\n\ndf['Attack_type'] = df['Attack_type'].str.replace('Port_Scanning', 'Scanning')\ndf['Attack_type'] = df['Attack_type'].str.replace('Fingerprinting', 'Scanning')\ndf['Attack_type'] = df['Attack_type'].str.replace('Vulnerability_scanner', 'Scanning')\n\ndf['Attack_type'] = df['Attack_type'].str.replace('MITM', 'MITM')\n\ndf['Attack_type'] = df['Attack_type'].str.replace('XSS', 'Injection')\ndf['Attack_type'] = df['Attack_type'].str.replace('SQL_injection', 'Injection')\ndf['Attack_type'] = df['Attack_type'].str.replace('Uploading', 'Injection')\n\ndf['Attack_type'] = df['Attack_type'].str.replace('Backdoor', 'Malware')\ndf['Attack_type'] = df['Attack_type'].str.replace('Password', 'Malware')\ndf['Attack_type'] = df['Attack_type'].str.replace('Ransomware', 'Malware')","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:20.659110Z","iopub.execute_input":"2023-11-15T09:53:20.659817Z","iopub.status.idle":"2023-11-15T09:53:33.228528Z","shell.execute_reply.started":"2023-11-15T09:53:20.659783Z","shell.execute_reply":"2023-11-15T09:53:33.227511Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"print(df['Attack_type'].value_counts())","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:33.230269Z","iopub.execute_input":"2023-11-15T09:53:33.230657Z","iopub.status.idle":"2023-11-15T09:53:33.478763Z","shell.execute_reply.started":"2023-11-15T09:53:33.230622Z","shell.execute_reply":"2023-11-15T09:53:33.477751Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Attack_type\nNormal       1399624\nDDoS          288112\nInjection     102851\nMalware        83648\nScanning       70856\nMITM             358\nName: count, dtype: int64\n","output_type":"stream"}]},{"cell_type":"code","source":"# Creating a dictionary of Types\nattacks = {'Normal':0, 'DDoS':1, 'Scanning':2, 'Injection':3, 'MITM':4, 'Malware':5}\ndf['Attack_type'] = df['Attack_type'].map(attacks)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:33.480266Z","iopub.execute_input":"2023-11-15T09:53:33.480592Z","iopub.status.idle":"2023-11-15T09:53:33.710482Z","shell.execute_reply.started":"2023-11-15T09:53:33.480567Z","shell.execute_reply":"2023-11-15T09:53:33.709473Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"X = df.drop(columns=['Attack_label', 'Attack_type'])\ny = df['Attack_type']","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:33.711946Z","iopub.execute_input":"2023-11-15T09:53:33.712301Z","iopub.status.idle":"2023-11-15T09:53:34.248281Z","shell.execute_reply.started":"2023-11-15T09:53:33.712269Z","shell.execute_reply":"2023-11-15T09:53:34.247407Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Apply the Chi-Squared test\nchi_selector = SelectKBest(chi2, k='all')  # Set k to the desired number of features\nX_kbest = chi_selector.fit_transform(X, y)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:34.249618Z","iopub.execute_input":"2023-11-15T09:53:34.250042Z","iopub.status.idle":"2023-11-15T09:53:37.678788Z","shell.execute_reply.started":"2023-11-15T09:53:34.250007Z","shell.execute_reply":"2023-11-15T09:53:37.678000Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"}]},{"cell_type":"code","source":"# Get the scores for each feature\nchi_scores = chi_selector.scores_\n\n# Combine scores with feature names\nchi_scores = pd.DataFrame({'feature': X.columns, 'score': chi_scores})\n\n# Sort the features by their scores\nchi_scores = chi_scores.sort_values(by='score', ascending=False)\n\nprint(chi_scores)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:37.679873Z","iopub.execute_input":"2023-11-15T09:53:37.680155Z","iopub.status.idle":"2023-11-15T09:53:37.782022Z","shell.execute_reply.started":"2023-11-15T09:53:37.680130Z","shell.execute_reply":"2023-11-15T09:53:37.781158Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"                    feature         score\n8               tcp.ack_raw  3.090813e+14\n7                   tcp.ack  1.448397e+14\n18                  tcp.seq  7.310286e+12\n20               udp.stream  9.184477e+11\n4   icmp.transmit_timestamp  1.689471e+11\n..                      ...           ...\n89                  mqtt1_8  1.169939e+00\n84                  mqtt1_3  7.799595e-01\n85                  mqtt1_4  7.799595e-01\n90                  mqtt1_9  7.799595e-01\n91                 mqtt1_10  7.799595e-01\n\n[97 rows x 2 columns]\n","output_type":"stream"}]},{"cell_type":"code","source":"selected_features = chi_scores['feature'].tolist()[:93]  # Select top k features","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:37.783449Z","iopub.execute_input":"2023-11-15T09:53:37.783858Z","iopub.status.idle":"2023-11-15T09:53:37.788410Z","shell.execute_reply.started":"2023-11-15T09:53:37.783830Z","shell.execute_reply":"2023-11-15T09:53:37.787465Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# Split the data into train (80%) and test (20%) sets\nX_train, X_test, y_train, y_test = train_test_split(X[selected_features], y, test_size=0.2, random_state=42)\n\n# Split the training data further into train (80%) and validation (20%) sets\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.125, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:37.789589Z","iopub.execute_input":"2023-11-15T09:53:37.789883Z","iopub.status.idle":"2023-11-15T09:53:41.184554Z","shell.execute_reply.started":"2023-11-15T09:53:37.789852Z","shell.execute_reply":"2023-11-15T09:53:41.183700Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"print(\"X_train shape:\", X_train.shape)\nprint(\"X_val shape:\", X_val.shape)\nprint(\"X_test shape:\", X_test.shape)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:41.185670Z","iopub.execute_input":"2023-11-15T09:53:41.185956Z","iopub.status.idle":"2023-11-15T09:53:41.191152Z","shell.execute_reply.started":"2023-11-15T09:53:41.185931Z","shell.execute_reply":"2023-11-15T09:53:41.190212Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"X_train shape: (1361814, 93)\nX_val shape: (194545, 93)\nX_test shape: (389090, 93)\n","output_type":"stream"}]},{"cell_type":"code","source":"scaler = StandardScaler().fit(X_train)\nX_train = scaler.transform(X_train)\nX_val = scaler.transform(X_val)\nX_test = scaler.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:41.194718Z","iopub.execute_input":"2023-11-15T09:53:41.195018Z","iopub.status.idle":"2023-11-15T09:53:43.537842Z","shell.execute_reply.started":"2023-11-15T09:53:41.194989Z","shell.execute_reply":"2023-11-15T09:53:43.536862Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"}]},{"cell_type":"code","source":"def cnn_lstm_gru_model(input_shape, num_classes):\n    \n    model = Sequential([\n        Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=input_shape),        \n        MaxPooling1D(pool_size=2),\n        \n        Conv1D(filters=64, kernel_size=3, activation='relu'),\n        MaxPooling1D(pool_size=2),\n        \n        LSTM(64, return_sequences=True),\n        GRU(64, return_sequences=False),\n        \n        Flatten(),\n        \n        Dense(128, activation='relu'),\n        Dropout(0.5),\n        Dense(num_classes, activation='softmax')\n    ])\n\n    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n    return model","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:43.539160Z","iopub.execute_input":"2023-11-15T09:53:43.539890Z","iopub.status.idle":"2023-11-15T09:53:43.547273Z","shell.execute_reply.started":"2023-11-15T09:53:43.539852Z","shell.execute_reply":"2023-11-15T09:53:43.546430Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"input_shape = (X_train.shape[1], 1)\nnum_classes = 6\nmodel = cnn_lstm_gru_model(input_shape, num_classes)\nmodel.summary()\nplot_model(model)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:43.548536Z","iopub.execute_input":"2023-11-15T09:53:43.548844Z","iopub.status.idle":"2023-11-15T09:53:46.713682Z","shell.execute_reply.started":"2023-11-15T09:53:43.548819Z","shell.execute_reply":"2023-11-15T09:53:46.712727Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv1d (Conv1D)             (None, 91, 32)            128       \n                                                                 \n max_pooling1d (MaxPooling1  (None, 45, 32)            0         \n D)                                                              \n                                                                 \n conv1d_1 (Conv1D)           (None, 43, 64)            6208      \n                                                                 \n max_pooling1d_1 (MaxPoolin  (None, 21, 64)            0         \n g1D)                                                            \n                                                                 \n lstm (LSTM)                 (None, 21, 64)            33024     \n                                                                 \n gru (GRU)                   (None, 64)                24960     \n                                                                 \n flatten (Flatten)           (None, 64)                0         \n                                                                 \n dense (Dense)               (None, 128)               8320      \n                                                                 \n dropout (Dropout)           (None, 128)               0         \n                                                                 \n dense_1 (Dense)             (None, 6)                 774       \n                                                                 \n=================================================================\nTotal params: 73414 (286.77 KB)\nTrainable params: 73414 (286.77 KB)\nNon-trainable params: 0 (0.00 Byte)\n_________________________________________________________________\n","output_type":"stream"},{"execution_count":16,"output_type":"execute_result","data":{"image/png":"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","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"cell_type":"code","source":"train_start_time = time.time()\n# Train the model\nhistory = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, batch_size=32)\n# Record the ending time\ntrain_end_time = time.time()\n\n# Record the starting time for testing\ntest_start_time = time.time()\n# Evaluate the model\nloss, accuracy = model.evaluate(X_test, y_test, batch_size=32)\n# Record the ending time for testing\ntest_end_time = time.time()\n\nprint(f'Test Loss: {loss:.4f}')\nprint(f'Test Accuracy: {accuracy:.4f}')\n\n# Calculate and print the training time\ntrain_time = train_end_time - train_start_time\nprint(f\"Training time: {train_time:.2f} seconds\")\n\n# Calculate and print the testing time\ntest_time = test_end_time - test_start_time\nprint(f\"Testing time: {test_time:.2f} seconds\")","metadata":{"execution":{"iopub.status.busy":"2023-11-15T09:53:46.714961Z","iopub.execute_input":"2023-11-15T09:53:46.715246Z","iopub.status.idle":"2023-11-15T14:01:12.559408Z","shell.execute_reply.started":"2023-11-15T09:53:46.715220Z","shell.execute_reply":"2023-11-15T14:01:12.558588Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Epoch 1/50\n42557/42557 [==============================] - 311s 7ms/step - loss: 0.0959 - accuracy: 0.9577 - val_loss: 0.0735 - val_accuracy: 0.9659\nEpoch 2/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0727 - accuracy: 0.9664 - val_loss: 0.0690 - val_accuracy: 0.9679\nEpoch 3/50\n42557/42557 [==============================] - 296s 7ms/step - loss: 0.0609 - accuracy: 0.9717 - val_loss: 0.0570 - val_accuracy: 0.9726\nEpoch 4/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0570 - accuracy: 0.9731 - val_loss: 0.0527 - val_accuracy: 0.9739\nEpoch 5/50\n42557/42557 [==============================] - 292s 7ms/step - loss: 0.0562 - accuracy: 0.9734 - val_loss: 0.0535 - val_accuracy: 0.9737\nEpoch 6/50\n42557/42557 [==============================] - 310s 7ms/step - loss: 0.0556 - accuracy: 0.9735 - val_loss: 0.0528 - val_accuracy: 0.9742\nEpoch 7/50\n42557/42557 [==============================] - 311s 7ms/step - loss: 0.0553 - accuracy: 0.9736 - val_loss: 0.0537 - val_accuracy: 0.9741\nEpoch 8/50\n42557/42557 [==============================] - 312s 7ms/step - loss: 0.0548 - accuracy: 0.9737 - val_loss: 0.0549 - val_accuracy: 0.9733\nEpoch 9/50\n42557/42557 [==============================] - 311s 7ms/step - loss: 0.0548 - accuracy: 0.9737 - val_loss: 0.0555 - val_accuracy: 0.9727\nEpoch 10/50\n42557/42557 [==============================] - 302s 7ms/step - loss: 0.0546 - accuracy: 0.9739 - val_loss: 0.0522 - val_accuracy: 0.9746\nEpoch 11/50\n42557/42557 [==============================] - 296s 7ms/step - loss: 0.0548 - accuracy: 0.9740 - val_loss: 0.0531 - val_accuracy: 0.9738\nEpoch 12/50\n42557/42557 [==============================] - 294s 7ms/step - loss: 0.0548 - accuracy: 0.9739 - val_loss: 0.0517 - val_accuracy: 0.9742\nEpoch 13/50\n42557/42557 [==============================] - 294s 7ms/step - loss: 0.0547 - accuracy: 0.9741 - val_loss: 0.0540 - val_accuracy: 0.9742\nEpoch 14/50\n42557/42557 [==============================] - 294s 7ms/step - loss: 0.0546 - accuracy: 0.9741 - val_loss: 0.0526 - val_accuracy: 0.9745\nEpoch 15/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0542 - accuracy: 0.9741 - val_loss: 0.0523 - val_accuracy: 0.9746\nEpoch 16/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0541 - accuracy: 0.9742 - val_loss: 0.0516 - val_accuracy: 0.9749\nEpoch 17/50\n42557/42557 [==============================] - 292s 7ms/step - loss: 0.0545 - accuracy: 0.9743 - val_loss: 0.0518 - val_accuracy: 0.9747\nEpoch 18/50\n42557/42557 [==============================] - 291s 7ms/step - loss: 0.0548 - accuracy: 0.9742 - val_loss: 0.0559 - val_accuracy: 0.9735\nEpoch 19/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0549 - accuracy: 0.9743 - val_loss: 0.0533 - val_accuracy: 0.9736\nEpoch 20/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0548 - accuracy: 0.9741 - val_loss: 0.0534 - val_accuracy: 0.9737\nEpoch 21/50\n42557/42557 [==============================] - 297s 7ms/step - loss: 0.0549 - accuracy: 0.9744 - val_loss: 0.0518 - val_accuracy: 0.9746\nEpoch 22/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0552 - accuracy: 0.9741 - val_loss: 0.0544 - val_accuracy: 0.9747\nEpoch 23/50\n42557/42557 [==============================] - 297s 7ms/step - loss: 0.0548 - accuracy: 0.9743 - val_loss: 0.0522 - val_accuracy: 0.9744\nEpoch 24/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0571 - accuracy: 0.9743 - val_loss: 0.0529 - val_accuracy: 0.9738\nEpoch 25/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0554 - accuracy: 0.9741 - val_loss: 0.0534 - val_accuracy: 0.9709\nEpoch 26/50\n42557/42557 [==============================] - 297s 7ms/step - loss: 0.0558 - accuracy: 0.9741 - val_loss: 0.0540 - val_accuracy: 0.9744\nEpoch 27/50\n42557/42557 [==============================] - 296s 7ms/step - loss: 0.0569 - accuracy: 0.9739 - val_loss: 0.0548 - val_accuracy: 0.9733\nEpoch 28/50\n42557/42557 [==============================] - 294s 7ms/step - loss: 0.0592 - accuracy: 0.9736 - val_loss: 0.0525 - val_accuracy: 0.9745\nEpoch 29/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0566 - accuracy: 0.9738 - val_loss: 0.0525 - val_accuracy: 0.9741\nEpoch 30/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0566 - accuracy: 0.9739 - val_loss: 0.0525 - val_accuracy: 0.9747\nEpoch 31/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0578 - accuracy: 0.9739 - val_loss: 0.0766 - val_accuracy: 0.9726\nEpoch 32/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0570 - accuracy: 0.9737 - val_loss: 0.0520 - val_accuracy: 0.9748\nEpoch 33/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0562 - accuracy: 0.9738 - val_loss: 0.0536 - val_accuracy: 0.9742\nEpoch 34/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0569 - accuracy: 0.9739 - val_loss: 0.0544 - val_accuracy: 0.9734\nEpoch 35/50\n42557/42557 [==============================] - 296s 7ms/step - loss: 0.0597 - accuracy: 0.9734 - val_loss: 0.0545 - val_accuracy: 0.9743\nEpoch 36/50\n42557/42557 [==============================] - 297s 7ms/step - loss: 0.0668 - accuracy: 0.9721 - val_loss: 0.0563 - val_accuracy: 0.9733\nEpoch 37/50\n42557/42557 [==============================] - 294s 7ms/step - loss: 0.0597 - accuracy: 0.9732 - val_loss: 0.0536 - val_accuracy: 0.9743\nEpoch 38/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0582 - accuracy: 0.9736 - val_loss: 0.0519 - val_accuracy: 0.9746\nEpoch 39/50\n42557/42557 [==============================] - 296s 7ms/step - loss: 0.0583 - accuracy: 0.9739 - val_loss: 0.0567 - val_accuracy: 0.9732\nEpoch 40/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0596 - accuracy: 0.9735 - val_loss: 0.0533 - val_accuracy: 0.9744\nEpoch 41/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0613 - accuracy: 0.9727 - val_loss: 0.0542 - val_accuracy: 0.9735\nEpoch 42/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0628 - accuracy: 0.9724 - val_loss: 0.0574 - val_accuracy: 0.9734\nEpoch 43/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0680 - accuracy: 0.9723 - val_loss: 0.0756 - val_accuracy: 0.9701\nEpoch 44/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0583 - accuracy: 0.9733 - val_loss: 0.0546 - val_accuracy: 0.9741\nEpoch 45/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0594 - accuracy: 0.9732 - val_loss: 0.0879 - val_accuracy: 0.9719\nEpoch 46/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0596 - accuracy: 0.9732 - val_loss: 0.0553 - val_accuracy: 0.9733\nEpoch 47/50\n42557/42557 [==============================] - 295s 7ms/step - loss: 0.0730 - accuracy: 0.9725 - val_loss: 0.0552 - val_accuracy: 0.9736\nEpoch 48/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0632 - accuracy: 0.9723 - val_loss: 0.0554 - val_accuracy: 0.9729\nEpoch 49/50\n42557/42557 [==============================] - 294s 7ms/step - loss: 0.0601 - accuracy: 0.9729 - val_loss: 0.0547 - val_accuracy: 0.9737\nEpoch 50/50\n42557/42557 [==============================] - 293s 7ms/step - loss: 0.0635 - accuracy: 0.9729 - val_loss: 0.0560 - val_accuracy: 0.9739\n12160/12160 [==============================] - 42s 3ms/step - loss: 0.0532 - accuracy: 0.9744\nTest Loss: 0.0532\nTest Accuracy: 0.9744\nTraining time: 14803.63 seconds\nTesting time: 42.20 seconds\n","output_type":"stream"}]},{"cell_type":"code","source":"# Plot the training and validation accuracy over the epochs\nplt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper left')\nplt.show()\nplt.savefig('acc_plo.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-15T14:01:12.560638Z","iopub.execute_input":"2023-11-15T14:01:12.560932Z","iopub.status.idle":"2023-11-15T14:01:12.882737Z","shell.execute_reply.started":"2023-11-15T14:01:12.560905Z","shell.execute_reply":"2023-11-15T14:01:12.881925Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"# Plot the training and validation loss over the epochs\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='upper left')\nplt.show()\nplt.savefig('los_plo.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-15T14:01:12.883684Z","iopub.execute_input":"2023-11-15T14:01:12.883933Z","iopub.status.idle":"2023-11-15T14:01:13.098858Z","shell.execute_reply.started":"2023-11-15T14:01:12.883911Z","shell.execute_reply":"2023-11-15T14:01:13.098097Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkAAAAHHCAYAAABXx+fLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/OQEPoAAAACXBIWXMAAA9hAAAPYQGoP6dpAACTTElEQVR4nO3dd3iTZfcH8O+TpE26W+iGQtl7L4sDEaSgIiAqIioiys+Boqjv68bx+uKeqLwucKOo4AQEBGTKXrL37KZ7JE2e3x93now2bbOapO33c1292iZPkiehNKfnPvc5kizLMoiIiIiaEJW/T4CIiIjI1xgAERERUZPDAIiIiIiaHAZARERE1OQwACIiIqImhwEQERERNTkMgIiIiKjJYQBERERETQ4DICIiImpyGAARUYMnSRKeffZZl2934sQJSJKE+fPn13rc6tWrIUkSVq9e7db5EVHgYQBERF4xf/58SJIESZKwbt26atfLsoyUlBRIkoRrrrnGD2dIRGTFAIiIvEqn0+Hrr7+udvmaNWtw5swZaLVaP5wVEZE9BkBE5FVXXXUVFi5ciMrKSrvLv/76a/Tr1w+JiYl+OjMiIisGQETkVRMnTkRubi6WL19uuUyv1+P777/HzTff7PA2JSUlePjhh5GSkgKtVotOnTrhtddegyzLdsdVVFTgoYceQlxcHCIiInDttdfizJkzDu/z7NmzuOOOO5CQkACtVotu3brh008/9d4TBbBw4UL069cPISEhiI2NxS233IKzZ8/aHZORkYEpU6agZcuW0Gq1SEpKwpgxY3DixAnLMVu3bkV6ejpiY2MREhKCNm3a4I477vDquRKRPY2/T4CIGpfU1FSkpaXhm2++wahRowAAS5YsQUFBAW666Sa88847dsfLsoxrr70Wq1atwtSpU9G7d28sW7YMjz76KM6ePYs333zTcuydd96JL7/8EjfffDMGDx6MP//8E1dffXW1c8jMzMRFF10ESZIwffp0xMXFYcmSJZg6dSoKCwvx4IMPevw858+fjylTpmDAgAGYPXs2MjMz8fbbb2P9+vXYsWMHoqOjAQDjx4/HP//8g/vvvx+pqanIysrC8uXLcerUKcv3I0aMQFxcHB577DFER0fjxIkT+PHHHz0+RyKqhUxE5AXz5s2TAchbtmyR58yZI0dERMilpaWyLMvyDTfcIA8dOlSWZVlu3bq1fPXVV1tut3jxYhmA/J///Mfu/q6//npZkiT5yJEjsizL8s6dO2UA8r333mt33M033ywDkGfNmmW5bOrUqXJSUpKck5Njd+xNN90kR0VFWc7r+PHjMgB53rx5tT63VatWyQDkVatWybIsy3q9Xo6Pj5e7d+8ul5WVWY779ddfZQDyM888I8uyLF+4cEEGIL/66qs13veiRYssrxsR+Q6XwIjI62688UaUlZXh119/RVFREX799dcal79+//13qNVqPPDAA3aXP/zww5BlGUuWLLEcB6DacVWzObIs44cffsDo0aMhyzJycnIsH+np6SgoKMD27ds9en5bt25FVlYW7r33Xuh0OsvlV199NTp37ozffvsNABASEoLg4GCsXr0aFy5ccHhfSqbo119/hcFg8Oi8iMh5DICIyOvi4uIwfPhwfP311/jxxx9hNBpx/fXXOzz25MmTSE5ORkREhN3lXbp0sVyvfFapVGjXrp3dcZ06dbL7Pjs7G/n5+fjwww8RFxdn9zFlyhQAQFZWlkfPTzmnqo8NAJ07d7Zcr9Vq8fLLL2PJkiVISEjAZZddhldeeQUZGRmW44cMGYLx48fjueeeQ2xsLMaMGYN58+ahoqLCo3MkotqxBoiI6sXNN9+Mu+66CxkZGRg1apQl01HfTCYTAOCWW27B5MmTHR7Ts2dPn5wLIDJUo0ePxuLFi7Fs2TI8/fTTmD17Nv7880/06dMHkiTh+++/x6ZNm/DLL79g2bJluOOOO/D6669j06ZNCA8P99m5EjUlzAARUb0YN24cVCoVNm3aVOPyFwC0bt0a586dQ1FRkd3lBw4csFyvfDaZTDh69KjdcQcPHrT7XtkhZjQaMXz4cIcf8fHxHj035ZyqPrZymXK9ol27dnj44Yfxxx9/YO/evdDr9Xj99dftjrnooovw4osvYuvWrfjqq6/wzz//YMGCBR6dJxHVjAEQEdWL8PBwfPDBB3j22WcxevToGo+76qqrYDQaMWfOHLvL33zzTUiSZNlJpnyuuovsrbfesvterVZj/Pjx+OGHH7B3795qj5edne3O07HTv39/xMfHY+7cuXZLVUuWLMH+/fstO9NKS0tRXl5ud9t27dohIiLCcrsLFy5U2+7fu3dvAOAyGFE94hIYEdWbmpagbI0ePRpDhw7Fk08+iRMnTqBXr174448/8NNPP+HBBx+01Pz07t0bEydOxPvvv4+CggIMHjwYK1euxJEjR6rd50svvYRVq1Zh0KBBuOuuu9C1a1fk5eVh+/btWLFiBfLy8jx6XkFBQXj55ZcxZcoUDBkyBBMnTrRsg09NTcVDDz0EADh06BCGDRuGG2+8EV27doVGo8GiRYuQmZmJm266CQDw2Wef4f3338e4cePQrl07FBUV4aOPPkJkZCSuuuoqj86TiGrGAIiI/EqlUuHnn3/GM888g2+//Rbz5s1DamoqXn31VTz88MN2x3766aeIi4vDV199hcWLF+OKK67Ab7/9hpSUFLvjEhISsHnzZjz//PP48ccf8f7776N58+bo1q0bXn75Za+c9+23347Q0FC89NJL+Pe//42wsDCMGzcOL7/8sqXeKSUlBRMnTsTKlSvxxRdfQKPRoHPnzvjuu+8wfvx4AKIIevPmzViwYAEyMzMRFRWFgQMH4quvvkKbNm28cq5EVJ0kV829EhERETVyrAEiIiKiJocBEBERETU5DICIiIioyWEARERERE0OAyAiIiJqchgAERERUZPDPkAOmEwmnDt3DhEREZAkyd+nQ0RERE6QZRlFRUVITk6GSlV7jocBkAPnzp2r1liNiIiIGobTp0+jZcuWtR7DAMiBiIgIAOIFjIyM9PPZEBERkTMKCwuRkpJieR+vDQMgB5Rlr8jISAZAREREDYwz5SssgiYiIqImhwEQERERNTkMgIiIiKjJYQ2QB4xGIwwGg79Pg7wgKCgIarXa36dBREQ+wgDIDbIsIyMjA/n5+f4+FfKi6OhoJCYmsvcTEVETwADIDUrwEx8fj9DQUL5hNnCyLKO0tBRZWVkAgKSkJD+fERER1TcGQC4yGo2W4Kd58+b+Ph3ykpCQEABAVlYW4uPjuRxGRNTIsQjaRUrNT2hoqJ/PhLxN+TdlXRcRUePHAMhNXPZqfPhvSkTUdDAAIiIioiaHARC5LTU1FW+99Za/T4OIiMhlDICaAEmSav149tln3brfLVu2YNq0ad49WSIiIh/gLjAfMppkGE0mSJKEILXvYs/z589bvv7222/xzDPP4ODBg5bLwsPDLV/Lsgyj0QiNpu4fjbi4OO+eKBERkY8wA+RDOcUVOJBRhMzCcp8+bmJiouUjKioKkiRZvj9w4AAiIiKwZMkS9OvXD1qtFuvWrcPRo0cxZswYJCQkIDw8HAMGDMCKFSvs7rfqEpgkSfj4448xbtw4hIaGokOHDvj55599+lyJiIicwQDIC2RZRqm+ss6PCoMJ5QYjSiqMTh1f14csy157Do899hheeukl7N+/Hz179kRxcTGuuuoqrFy5Ejt27MDIkSMxevRonDp1qtb7ee6553DjjTdi9+7duOqqqzBp0iTk5eV57TyJiIi8gUtgXlBmMKLrM8t8/rj7nk9HaLB3/gmff/55XHnllZbvmzVrhl69elm+f+GFF7Bo0SL8/PPPmD59eo33c/vtt2PixIkAgP/+97945513sHnzZowcOdIr50lEROQNzAARAKB///523xcXF+ORRx5Bly5dEB0djfDwcOzfv7/ODFDPnj0tX4eFhSEyMtIyYoKIiChQMAPkBSFBaux7Pr3O4wrLDDiVVwpdkBrt48PrPN6Zx/WWsLAwu+8feeQRLF++HK+99hrat2+PkJAQXH/99dDr9bXeT1BQkN33kiTBZDJ57TyJiIi8gQGQF0iS5NRSlCwDuiA1tBqV15au6sv69etx++23Y9y4cQBERujEiRP+PSkiIiIv4RKYD6nMoxaMDSAh0qFDB/z444/YuXMndu3ahZtvvpmZHCIiajQYAPmQ0vrH5MXdW/XljTfeQExMDAYPHozRo0cjPT0dffv29fdpEREReYUke3MvdSNRWFiIqKgoFBQUIDIy0u668vJyHD9+HG3atIFOp3PpfiuNJuw7XwgA6NEiisM3A4wn/7ZEROR/tb1/V8UMkA+pVNaAx2hi3ElEROQvDIB8SGWevQUAjH+IiIj8hwGQj6nNSaCGUAdERETUWDEA8jFlGYxLYERERP7DAMjHVJYlMAZARERE/sIAyMfUSgDEDBAREZHfMADyMcsSGOMfIiIiv2EA5GMsgiYiIvI/BkA+xiJoIiIi/2MA5GMNuQj68ssvx4MPPmj5PjU1FW+99Vatt5EkCYsXL/b4sb11P0RERAADIJ9Tq/xTBD169GiMHDnS4XVr166FJEnYvXu3S/e5ZcsWTJs2zRunZ/Hss8+id+/e1S4/f/48Ro0a5dXHIiKiposBkI9ZJsL7OAE0depULF++HGfOnKl23bx589C/f3/07NnTpfuMi4tDaGiot06xVomJidBqtT55LCIiavwYAPmYSpkI7+MM0DXXXIO4uDjMnz/f7vLi4mIsXLgQY8eOxcSJE9GiRQuEhoaiR48e+Oabb2q9z6pLYIcPH8Zll10GnU6Hrl27Yvny5dVu8+9//xsdO3ZEaGgo2rZti6effhoGgwEAMH/+fDz33HPYtWsXJPPYEOV8qy6B7dmzB1dccQVCQkLQvHlzTJs2DcXFxZbrb7/9dowdOxavvfYakpKS0Lx5c9x3332WxyIioqZN4+8TaBRkGTCUOnWo2qCHZCiDSaUB9B4+blAo4OREeY1Gg9tuuw3z58/Hk08+aZlJtnDhQhiNRtxyyy1YuHAh/v3vfyMyMhK//fYbbr31VrRr1w4DBw6s8/5NJhOuu+46JCQk4O+//0ZBQYFdvZAiIiIC8+fPR3JyMvbs2YO77roLERER+Ne//oUJEyZg7969WLp0KVasWAEAiIqKqnYfJSUlSE9PR1paGrZs2YKsrCzceeedmD59ul2At2rVKiQlJWHVqlU4cuQIJkyYgN69e+Ouu+5y6jUjIqLGiwGQNxhKgf8mO3VotPnDK544BwSHOX34HXfcgVdffRVr1qzB5ZdfDkAsf40fPx6tW7fGI488Yjn2/vvvx7Jly/Ddd985FQCtWLECBw4cwLJly5CcLF6L//73v9Xqdp566inL16mpqXjkkUewYMEC/Otf/0JISAjCw8Oh0WiQmJhY42N9/fXXKC8vx+eff46wMPH858yZg9GjR+Pll19GQkICACAmJgZz5syBWq1G586dcfXVV2PlypUMgIiIiEtgTUnnzp0xePBgfPrppwCAI0eOYO3atZg6dSqMRiNeeOEF9OjRA82aNUN4eDiWLVuGU6dOOXXf+/fvR0pKiiX4AYC0tLRqx3377be4+OKLkZiYiPDwcDz11FNOP4btY/Xq1csS/ADAxRdfDJPJhIMHD1ou69atG9RqteX7pKQkZGVlufRYRETUODED5A1BoSIb44QyfSWOZJdAo1ahS2KE54/roqlTp+L+++/He++9h3nz5qFdu3YYMmQIXn75Zbz99tt466230KNHD4SFheHBBx+EXu/pOp3Vxo0bMWnSJDz33HNIT09HVFQUFixYgNdff91rj2ErKCjI7ntJkmAymerlsYiIqGFhAOQNkuT0UpRKZYQcJMPowm286cYbb8SMGTPw9ddf4/PPP8c999wDSZKwfv16jBkzBrfccgsAUdNz6NAhdO3a1an77dKlC06fPo3z588jKSkJALBp0ya7YzZs2IDWrVvjySeftFx28uRJu2OCg4NhNBrrfKz58+ejpKTEkgVav349VCoVOnXq5NT5EhFR08YlMB9T2zRClP3QDDE8PBwTJkzA448/jvPnz+P2228HAHTo0AHLly/Hhg0bsH//fvzf//0fMjMznb7f4cOHo2PHjpg8eTJ27dqFtWvX2gU6ymOcOnUKCxYswNGjR/HOO+9g0aJFdsekpqbi+PHj2LlzJ3JyclBRUVHtsSZNmgSdTofJkydj7969WLVqFe6//37ceuutlvofIiKi2jAA8jFlFAbgv27QU6dOxYULF5Cenm6p2XnqqafQt29fpKen4/LLL0diYiLGjh3r9H2qVCosWrQIZWVlGDhwIO688068+OKLdsdce+21eOihhzB9+nT07t0bGzZswNNPP213zPjx4zFy5EgMHToUcXFxDrfih4aGYtmyZcjLy8OAAQNw/fXXY9iwYZgzZ47rLwYRETVJkuyPNESAKywsRFRUFAoKChAZGWl3XXl5OY4fP442bdpAp9O5df97zhZAlmV0ToxEsIYxaKDwxr8tERH5T23v31Xx3dcPVJwIT0RE5FcMgPxAqQPiRHgiIiL/YADkB0odEDNARERE/sEAyA8sO8GYASIiIvILBkBu8qR2XMkA+XoiPNWO+wGIiJoOBkAuUroLl5Y6N/zUERZBBybl37RqB2kiImp82AnaRWq1GtHR0ZaZUqGhoZbJ6s6SDXrIlQZUlEso1zAI8jdZllFaWoqsrCxER0fbzQ8jIqLGiQGQG5RJ5e4O1swvNaC4ohJlOg0KQ5htCBTR0dG1TqEnIqLGgwGQGyRJQlJSEuLj42EwGFy+/bz1x/HlpnMY3SsZDw5vUw9nSK4KCgpi5oeIqAlhAOQBtVrt1pumOigYZ4uMyCwxseMwERGRH7AI2g/CtWLZq7ii0s9nQkRE1DQxAPKDMK3IGhWXMwAiIiLyB78HQO+99x5SU1Oh0+kwaNAgbN68udbjFy5ciM6dO0On06FHjx74/fff7a7PzMzE7bffjuTkZISGhmLkyJE4fPhwfT4Fl0XoxMpjiZ4BEBERkT/4NQD69ttvMXPmTMyaNQvbt29Hr169kJ6eXuPuqg0bNmDixImYOnUqduzYgbFjx2Ls2LHYu3cvALGdeezYsTh27Bh++ukn7NixA61bt8bw4cNRUlLiy6dWq7BgEQAxA0REROQfkuzH9reDBg3CgAEDMGfOHACAyWRCSkoK7r//fjz22GPVjp8wYQJKSkrw66+/Wi676KKL0Lt3b8ydOxeHDh1Cp06dsHfvXnTr1s1yn4mJifjvf/+LO++806nzKiwsRFRUFAoKChAZGemFZ2pv79kCXPPuOsRHaLH5yeFev38iIqKmyJX3b79lgPR6PbZt24bhw60BgEqlwvDhw7Fx40aHt9m4caPd8QCQnp5uOb6iogIA7HZWqVQqaLVarFu3rsZzqaioQGFhod1HfVKWwFgETURE5B9+C4BycnJgNBqRkJBgd3lCQgIyMjIc3iYjI6PW4zt37oxWrVrh8ccfx4ULF6DX6/Hyyy/jzJkzOH/+fI3nMnv2bERFRVk+UlJSPHx2tQvTigCoVG/kQFQiIiI/8HsRtDcFBQXhxx9/xKFDh9CsWTOEhoZi1apVGDVqFFSqmp/q448/joKCAsvH6dOn6/U8w7XW9ksshCYiIvI9vzVCjI2NhVqtRmZmpt3lmZmZNY4jSExMrPP4fv36YefOnSgoKIBer0dcXBwGDRqE/v3713guWq0WWq3Wg2fjGq1GBY1KQqVJRnFFJSJ0HIdBRETkS37LAAUHB6Nfv35YuXKl5TKTyYSVK1ciLS3N4W3S0tLsjgeA5cuXOzw+KioKcXFxOHz4MLZu3YoxY8Z49wl4QJIkhCtb4VkHRERE5HN+HYUxc+ZMTJ48Gf3798fAgQPx1ltvoaSkBFOmTAEA3HbbbWjRogVmz54NAJgxYwaGDBmC119/HVdffTUWLFiArVu34sMPP7Tc58KFCxEXF4dWrVphz549mDFjBsaOHYsRI0b45TnWJCxYg/xSA4q4FZ6IiMjn/BoATZgwAdnZ2XjmmWeQkZGB3r17Y+nSpZZC51OnTtnV7gwePBhff/01nnrqKTzxxBPo0KEDFi9ejO7du1uOOX/+PGbOnInMzEwkJSXhtttuw9NPP+3z51YXpQ6opMLo5zMhIiJqevzaByhQ1XcfIAAY/8EGbDt5AXNv6YeR3R3XPBEREZHzGkQfoKZO2QrPXkBERES+xwDITyK0LIImIiLyFwZAfmKZCM8AiIiIyOcYAPlJuFb0/mEARERE5HsMgPwkXMkAcRs8ERGRzzEA8pMw1gARERH5DQMgPwnnRHgiIiK/YQDkJ+HcBk9ERJ4ysZmuuxgA+Uk4l8CIiMgTe74HZrcEDi3z95k0SAyA/ESpASpiAERERO44sQ4wlAInN/j7TBokBkB+wgwQERF5pLJcfDaU+fc8GigGQH7CYahEROQRQ6n4XMkAyB0MgPzEdhaYycR5tERE5CIDM0CeYADkJxHmbfAAUGpgFoiIiFykZH4YALmFAZCfaDUqqFUSAHaDJiIiNxgYAHmCAZCfSJLEXkBEROQ+ZQlMKYYmlzAA8iMGQERE5DalCFr5TC5hAORHYeaBqNwKT0RELrNsg2cGyB0MgPyIGSAiInKbpQaIGSB3MADyI8tWeBZBExGRq1gE7REGQH6kbIUv0TMAIiIiF8iydRs8i6DdwgDIj8KCzfPAmAEiIiJXVFZYv+YSmFsYAPlRuI7zwIiIyA22QY+pEjAa/HcuDRQDID/iQFQiInJL1WUv1gG5jAGQHylF0EUMgIiIyBVVAx7WAbmMAZAfMQNERERuqRoAsQ7IZQyA/Ih9gIiIyC3VlsCYAXIVAyA/sgZAnAZPREQuYAbIYwyA/MjaCJHV+0RE5ALWAHmMAZAfWRohMgNERESuqGQGyFMMgPwojEXQRETkjqo1P6wBchkDID9SpsEX6yshy7Kfz4aIiBqMqhkf9gFyGQMgP4rQBgEQI11K9VwGIyIiJ1XbBcYlMFcxAPIjXZAKKkl8za3wRETkNBZBe4wBkB9JksReQERE5Dpug/cYAyA/YzdoIiJyWbVdYMwAuYoBkJ8pE+GLyxkAERGRk6rtAmMGyFUMgPwsjEtgRETkKmUJTCXeQ1gD5DoGQH7GGiAiInKZsgQW0kx8ZgbIZQyA/Iw1QERE5DIlAxQSY/6eGSBXMQDyM2UJrIgBEBEROUsJgEKb2X9PTmMA5GfMABERkcuUmh9lCazqrjCqEwMgP7MGQOwETURETlJqfkKVJTAGQK5iAORnliUwboMnIiJnGapkgBgAuYwBkJ8pfYC4BEZERE6rZA2QpxgA+Vm4MhGeARARETnLUHUbPAMgVzEA8rNw80R4BkBEROQ0ZQkslEXQ7mIA5Gdh5gwQl8CIiMhp1RohMgByFQMgP4tgBoiIiFxhNAAm83uGbQ2QLPvvnBogBkB+FsYaICIicoVttkfJAMlGERiR0xgA+ZltI0SZ0TsREdXFMvhUAnRRNpdzGcwVDID8TNkGb5KBMgObIRIRUR2UJohBIYBGC0AyX84AyBUMgPwsJEgNlflnt5jNEImIqC7KDjCNDpAkICjUfDkDIFcwAPIzSZIs3aBZB0RERHVSlrqCQuw/MwByCQOgAMB5YERE5DRDDQEQa4BcwgAoACgBUFEFK/iJiKgOSgCkYQbIEwyAAkAYM0BEROQsZRdYkE581pg/K7VB5BQGQAEg3FIDxAwQERHVodoSmFIEXeqf82mgGAAFAGsAxAwQERHVodoSmM7+cnIKA6AAEGbTDJGIiKhWVZfAlAwQi6BdwgAoAESYmyGyDxAREdXJ0gjRHPhomAFyBwOgAMB5YERE5DTbRogAGyG6iQFQAAjnRHgiInKW7SgMgDVAbmIAFADCzRkg1gAREVGdLDVAbIToCb8HQO+99x5SU1Oh0+kwaNAgbN68udbjFy5ciM6dO0On06FHjx74/fff7a4vLi7G9OnT0bJlS4SEhKBr166YO3dufT4Fj3EUBhEROa3aLjAugbnDrwHQt99+i5kzZ2LWrFnYvn07evXqhfT0dGRlZTk8fsOGDZg4cSKmTp2KHTt2YOzYsRg7diz27t1rOWbmzJlYunQpvvzyS+zfvx8PPvggpk+fjp9//tlXT8tl4QyAiIjIWZY+QGyE6Am/BkBvvPEG7rrrLkyZMsWSqQkNDcWnn37q8Pi3334bI0eOxKOPPoouXbrghRdeQN++fTFnzhzLMRs2bMDkyZNx+eWXIzU1FdOmTUOvXr3qzCz5Uzi3wRMRkbOqDUNlI0R3+C0A0uv12LZtG4YPH249GZUKw4cPx8aNGx3eZuPGjXbHA0B6errd8YMHD8bPP/+Ms2fPQpZlrFq1CocOHcKIESPq54l4QTi3wRMRkbMsu8CqFEFXMgPkCo2/HjgnJwdGoxEJCQl2lyckJODAgQMOb5ORkeHw+IyMDMv37777LqZNm4aWLVtCo9FApVLho48+wmWXXVbjuVRUVKCiosLyfWFhoTtPyW2sASIiIqdxFIZX+L0I2tveffddbNq0CT///DO2bduG119/Hffddx9WrFhR421mz56NqKgoy0dKSooPzxiIsAmAZFn26WMTEVEDU3UJjI0Q3eK3DFBsbCzUajUyMzPtLs/MzERiYqLD2yQmJtZ6fFlZGZ544gksWrQIV199NQCgZ8+e2LlzJ1577bVqy2eKxx9/HDNnzrR8X1hY6NMgSMkAmWSg3GBCSLDaZ49NREQNDBsheoXfMkDBwcHo168fVq5cabnMZDJh5cqVSEtLc3ibtLQ0u+MBYPny5ZbjDQYDDAYDVCr7p6VWq2EymWo8F61Wi8jISLsPXwoNVkOSxNdcBiMiolpVHYXBRohu8VsGCBBb1idPnoz+/ftj4MCBeOutt1BSUoIpU6YAAG677Ta0aNECs2fPBgDMmDEDQ4YMweuvv46rr74aCxYswNatW/Hhhx8CACIjIzFkyBA8+uijCAkJQevWrbFmzRp8/vnneOONN/z2POsiSRLCgzUoqqhEcUUl4iK0/j4lIiIKVNWGoYbYX05O8WsANGHCBGRnZ+OZZ55BRkYGevfujaVLl1oKnU+dOmWXzRk8eDC+/vprPPXUU3jiiSfQoUMHLF68GN27d7ccs2DBAjz++OOYNGkS8vLy0Lp1a7z44ou4++67ff78XBGmFQEQt8ITEVGtqjZCVD6zCNolksyq22oKCwsRFRWFgoICny2HDX9jDY5kFeObuy5CWrvmPnlMIiJqgF6IB4wVwIN7gegUIO848E5vICgMePKcv8/Or1x5/250u8AaqjA2QyQiorqYTCL4ARxvg2dOw2kMgAKEMhCVRdBERFQj2zofyy4w82fIgFHv81NqqBgABQjOAyMiojrZ7vSqmgECWAfkAgZAAYJLYEREVCelCaI6GFCZe8apgwDJ/DUHojqNAVCAiGAGiIiI6lJ1DpiC4zBcxgAoQHAeGBER1cnSBLFqAMSBqK5iABQgOBGeiIjqVLUJokIJiNgN2mkMgAKEUgRdomcARERENajaBFHBZoguYwAUIMKClSUwo5/PhIiIApahyiR4hSUDxCUwZzEAChDWJTCDn8+EiIgCVmVdARAzQM5iABQgLEtgzAAREVFNLEtgNdQAsQjaaQyAAgQbIRIRUZ1qWgJjDZDLGAAFCG6DJyKiOll2gbEGyFMMgAJEhM4aAMkcZkdERI4oGZ6alsCYAXIaA6AAoWSAjCYZFZUmP58NEREFJCXDYzv/C2ANkBsYAAWI0CC15WsugxERkUNshOg1DIAChEolWQuh2Q2aiIgcsYzCqJIB0jAAchUDoAASphVZIGaAiIjIIcswVGaAPMUAKIBwKzwROS1zH/BGN2DbZ/4+E/KluhohVjIAchYDoABibYbIAIiI6nBiLVB4Bjjwm7/PhHypzlEYDICcxQAogITrmAEiIidVFInP+mL/ngf5Foeheg0DoABiHYjKAIiI6qAEPgyAmpY6d4FxG7yzGAAFEC6BEZHTKortP1PTYNkFVnUJzBwQcQnMaQyAAoh1IjwDICKqAzNATZNlF1jVAMi8LZ5F0E5jABRArPPAOBGeiOpgqQEq8e95kG+xEaLXMAAKINZt8AY/nwkRBTzbDJCJ43MalPO7gRPr3LstGyF6DQOgAGKtAWIGiIjqYFv7Y2AWqMGQZeCLccDnY4GyC67fno0QvYYBUABhI0Qicppt7Q+XwRoOQxlQmgOYDEBxlmu3lWXnGiHKsufn2QQwAAogYQyAiMhZtkEPd4I1HOX5Nl8XunZbox6QzcudNQVAACfCO8mtAOj06dM4c+aM5fvNmzfjwQcfxIcffui1E2uKuA2eiJymFEEDgL6o5uMosJTlW7+uKHDttrbLWzU1Qqx6HNXIrQDo5ptvxqpVqwAAGRkZuPLKK7F582Y8+eSTeP755716gk2Jsg2+iNvgiag2sswlsIbKtu6nwsXAVcnsSCpAHWR/nVoDqMyXMQByilsB0N69ezFw4EAAwHfffYfu3btjw4YN+OqrrzB//nxvnl+TEm6eBl+iZwBERLWorABMNr8nuATWcHiyBGa7A0ySql/PQmiXuBUAGQwGaLVaAMCKFStw7bXXAgA6d+6M8+fPe+/smphwrYjeSyoqIbOIjYhqUrX5IZshNhx2S2CuBkA17ABTcCK8S9wKgLp164a5c+di7dq1WL58OUaOHAkAOHfuHJo3b+7VE2xKwswZIINRRkUl+3oQUQ2qLp0wAGo4PMkA1bQDTKHhOAxXuBUAvfzyy/jf//6Hyy+/HBMnTkSvXr0AAD///LNlaYxcpwxDBVgITUS1qBrwcAms4bDLALlYA2SoIwBSmiMyAHKKpu5Dqrv88suRk5ODwsJCxMTEWC6fNm0aQkNDa7kl1UalkhAWrEaJ3ojiiko0D9f6+5SIKBBVDXiYAWo4bDNA9bUExgDIKW5lgMrKylBRUWEJfk6ePIm33noLBw8eRHx8vFdPsKlhLyAiqhNrgBou2wxQuavb4GsYg6FgDZBL3AqAxowZg88//xwAkJ+fj0GDBuH111/H2LFj8cEHH3j1BJsaToQnojpVXTrhEljD4UkGqKZBqApmgFziVgC0fft2XHrppQCA77//HgkJCTh58iQ+//xzvPPOO149wabG0gyRW+GJqCZV+/4wA9Rw2GWAXF0CMwc2VZsgKlgE7RK3AqDS0lJEREQAAP744w9cd911UKlUuOiii3Dy5EmvnmBTY50HxoGoRFSDaktgbITYYNhlgFgE7U9uBUDt27fH4sWLcfr0aSxbtgwjRowAAGRlZSEyMtKrJ9jUWGqAuARGRDVRlryCI8zfcxRGg+FJH6C6tsErS2OsAXKKWwHQM888g0ceeQSpqakYOHAg0tLSAIhsUJ8+fbx6go2Oocz+P0AVnAdGRHVSZn9FJJi/5xJYgyDLHnaCrmsXGDNArnArALr++utx6tQpbN26FcuWLbNcPmzYMLz55pteO7lGZ82rwEutgU01F4orAVARAyAiqomSAYpIEp+5BNYwGMrERHeFsUKMNXH69sousLpqgDgN3hluBUAAkJiYiD59+uDcuXOWyfADBw5E586dvXZyjU54vPiBP7aqxkPCmAEiorooGZ+IRPGZu8AaBiX7I6ltLnMhC2TZBVZXDVCpy6fWFLkVAJlMJjz//POIiopC69at0bp1a0RHR+OFF16AycQRDjVqN1R8PrO1xmWwCB0DICKqgxLwhCtLYMwANQjK7/2QaCA4XHztSh2QZRdYTUtg3AXmCrc6QT/55JP45JNP8NJLL+Hiiy8GAKxbtw7PPvssysvL8eKLL3r1JBuN6FZA8w5A7mHgxFqgy+hqh4QFi78MuARGRDWy1AAlWb+XZccTwilwKBkgXbQIUvTF7gVANTZCNF/uRhF0ZmE5wrUayypEU+DWM/3ss8/w8ccfW6bAA0DPnj3RokUL3HvvvQyAatPuChEAHf3TYQAUrrNOhCcicqiiyhKYbBJvjsEcRRTQbDNA6iCg6JybS2DebYSYV6LHZa+sQqfECPw8/RKXbtuQubUElpeX57DWp3PnzsjLy/P4pBo1ZRns6J8Orw43T4TnNngiqpFSAxRuM3qIy2CBzzYDpDW3jHFrCcy7jRCPZhejotKEPWcLoK9sOmUsbgVAvXr1wpw5c6pdPmfOHPTs2dPjk2rUUi8BVBrgwgkg71i1qyPMGaDCcoOPT4yIGgwl2NFGAEFh5svYCyjg2WaAdEoA5MK/Wz01QswuEjvRZBnIKGg6O8jcWgJ75ZVXcPXVV2PFihWWHkAbN27E6dOn8fvvv3v1BBsdbQSQMgg4uV5kgZq1tbs6LkJMgFd+IImIqrFthKgNBwwl3AnWENhmgGRzpsWlJTBnGyG6FsTkFFvfb85cKEWr5k1jKdWtDNCQIUNw6NAhjBs3Dvn5+cjPz8d1112Hf/75B1988YW3z7HxsSyDVd8OnxAhfoAvlBpQbuA4DCKqQpat2R5tuHU3EZfAAp9tBsitJTBnGyG6tg0+p8g2AGo6O8jcLvdOTk6uVuy8a9cufPLJJ/jwww89PrFGrd0VwJ//AY7/BRgrAbX1nyEyRAOtRoWKShOyiyqQ0qxpROJE5CRDmTV7EBwOBCtLYMwABbyyC+KzLtraELG8wPnbWxoh1vC+4GYjxOwqGaCmwu1GiOSBpN5ASIyI/M9us7tKkiQkRokf4szCprMWS0ROsgQ6kgh+tJwH1mAoS2Ah0YA2SnztSgaozl1g7mWAsous3anP5DedDBADIH9QqYG2l4uvHewGU5bBMgtZB0REVSiBTnC46PvDJbCGQ1kC00W7WQStLIHVZw0QAyCqb21r3g4fHykKoZkBIqJqlAyQ1hz4cAms4bDLAJkzd64UQdc1C8zSCLEccGEqg+2mm7NNKAByqQbouuuuq/X6/Px8T86laVEKoc+ax2KERFuuSojkEhgR1cCyA8wcACmBEHeBBT7bDJAS+Di7BGYyAiZze5S6hqECIghyojGmLMt2GaCMwnJUGk3QqBt/fsSlACgqKqrO62+77TaPTqjJsB2LcfwvoKu1q3YCM0BEVJNqGaAI+8spMMmyfQZI+drZDJBtb58ad4HZBEZOdgYvrqhEhbn5oUYlodIk43xBeZPYgONSADRv3rz6Oo+mSRmLcWxVlQCINUBEVAPbGiCAS2ANhaHMuvMrJAYoNU9NcDYD5EwApFID6mDxOIZSAM3rvFtl+Stcq0FseDBO5JbizIWyJhEANf4cVyBrd4X4XKUOyBIAFTEDRERVKMXOXAJrWJSMj6QW/3au7t6rtJkEr6rlrVvJAjlZCJ1TLIKyuAgtWsaIoKepbIVnAORPqZcAqqBqYzGUACiLGSAiqqraEli4/eUUmGybIEoSoDOXlOiLRX1PXeoag6FwcSu8Uv8TGx6MljHivs82ka3wDID8SRsuxmIAdlmgePM4jOKKShRzKjwR2apaBM0AqGGwHYMBWDtBA84tg9U1CFXhYjNEZQksNlxrCYCaylZ4BkD+1u5y8dlmLEaYVoMIrSjPYiE0EdmxHYNh+5lLYIHNNgMEAJpga7DiTCF0XU0QFW5mgOIitGhhCYC4BEa+oNQBHf8LMFonwLMXEBE5ZDsIFWAjxIaiagYIsJkH5kQdUF1jMBQuNkO0zwCJ++YSGPlGDWMxlHEYrAMiIjusAWqYqmaAAJtCaGeWwOoYhKpwuwbIugR2Pl/0AmrsGAD5m91YDOsymHUcBjNARGSjxkaInAUW0BxlgJRxGE4tgTlZBO1qDZDNLrD4CB2C1KIXUGZR4//jOyACoPfeew+pqanQ6XQYNGgQNm/eXOvxCxcuROfOnaHT6dCjRw/8/vvvdtdLkuTw49VXX63Pp+E+B9vh4807wTIYABGRrRozQFwCC2gOM0DKEpgLRdB17gIzX+9sBqjIugtMrZKQFGWuA8pr/HVAfg+Avv32W8ycOROzZs3C9u3b0atXL6SnpyMrK8vh8Rs2bMDEiRMxdepU7NixA2PHjsXYsWOxd+9eyzHnz5+3+/j0008hSRLGjx/vq6flmrZVxmLA2g2aS2BEZMfSCFGpATI3QjQZgEr+vghYtWaACuq+vcGmD1BtXOgDJMsysm2WwAA0qa3wfg+A3njjDdx1112YMmUKunbtirlz5yI0NBSffvqpw+PffvttjBw5Eo8++ii6dOmCF154AX379sWcOXMsxyQmJtp9/PTTTxg6dCjatm3rq6flmugUMRZDNoliaHAeGBHVQMkAKYGPkgECuBMskDnMAJl7ATmzfGnZBeZsBqjuAKaoohJ68xiMuAj7AKgpbIX3awCk1+uxbds2DB8+3HKZSqXC8OHDsXHjRoe32bhxo93xAJCenl7j8ZmZmfjtt98wderUGs+joqIChYWFdh8+V2UZjN2gicghZalLWQJTa6y9YVgIHbgc7gJzpQja2Rog55fAlB1gEVoNdEFqAGhS3aD9GgDl5OTAaDQiISHB7vKEhARkZGQ4vE1GRoZLx3/22WeIiIiodZL97NmzERUVZflISUlx8Zl4gSUAWgnIss1A1ArIsuz78yGiwFS1CBrgPLCGwFEGyJUiaGcbIVoyQHX/8Wyp/zFnfwCgRTSXwBqNTz/9FJMmTYJOV/O66eOPP46CggLLx+nTp314hmbKWIz8U0DeMUs6Ul9pQn6poY4bE1GTIMs2RdAR1svZDDHw1doHyD+NEC1zwMKtARCXwHwkNjYWarUamZmZdpdnZmYiMTHR4W0SExOdPn7t2rU4ePAg7rzzzlrPQ6vVIjIy0u7D52zHYhxbBa1GjWZhwQC4DEZEZvoSAOaMsF0GiL2AAposA2UXxNduZ4C83wgx2/zeEhsRbLmspXkK/Ln8MhhNjXv1wa8BUHBwMPr164eVK1daLjOZTFi5ciXS0tIc3iYtLc3ueABYvny5w+M/+eQT9OvXD7169fLuideXdubdYOZ+QMpMsEzuBCMiwBrgSCr7WhAGQIHNUAYYRbbFcQ2QM52gnW2E6HwNkJIBirXJACVEaKFWSTAYZWQ18j++/b4ENnPmTHz00Uf47LPPsH//ftxzzz0oKSnBlClTAAC33XYbHn/8ccvxM2bMwNKlS/H666/jwIEDePbZZ7F161ZMnz7d7n4LCwuxcOHCOrM/AaXKWAzuBCMiO7ZjMCTJermWvYACmrL8JamrLF26sgTmahG0EzVAyhwwmwBIo1YhyTyJ4GwjXwbT+PsEJkyYgOzsbDzzzDPIyMhA7969sXTpUkuh86lTp6BSWeO0wYMH4+uvv8ZTTz2FJ554Ah06dMDixYvRvXt3u/tdsGABZFnGxIkTffp8PJLUS4zFKLsAnN2GxEiRisxiAEREQPVBqAqlCJo1QIFJKYDWRdkHrjrzNnhXiqC9uA0+20ERNCDqgM5cKMOZC2Xon1r3qTVUfg+AAGD69OnVMjiK1atXV7vshhtuwA033FDrfU6bNg3Tpk3zxun5jjIW459FwNE/kRApGjeyGzQRAXC8AwywNkXUcxxGQFIyQLb1P4BNBqg+GiHWHQA5ygABylb4vEa/Fd7vS2BUhbIMdmyNZRwGa4CICED1MRgKLoEFNssW+Bj7y3U20+DrandSD40Qa8oANZWt8AyAAk2CeSkv/6SlBohLYEQEwCYDFGZ/OZfAApujLfCAtR5INtUdvLrcCLH24EWWZZsi6GC765rKVngGQIEmIkl8Ls5EQrhYoWQGiIgA2IzBiLC/nLvAApujJoiA2NIuiQ7MdRZCu9wIsfbgpbC8EnqjGIMR63AJjAEQ+VpYnNjiKpuQrBG/zLKLKxp9PwYickKNS2AR9tdTYKkpAyRJzvcCcnUJrI4aIMsYDJ11DIbCMhD1QhlMjfi9hwFQoFFrgLB4AECMKQ8qCTCaZOQWMwtE1OTVWATNJbCAVlMGCHB+K7ylEaJ3MkA1FUADQGKUDioJ0BtNluMaIwZAgShCdLVWl2RYRmJwGYyIaswAcQkssNWUAQJsCqHrCoCcbYRo7hRt1AMmY42HKYFN1QJoAAhSq5AUJQKp0414GYwBUCBS6oCKMtgMkYislI7BVWuAuAsssDmTAaptCUyWbRoh1jEKwzZAqiULpCyBOcoAAUALSyF0490KzwAoEJkzQCjKQHyEOQBq5C3JicgJdWWAnBmpQL5XJQM066e9uO+r7aK205klMNu5XnUNQ3UyALJkgKrsAFO0jG78O8EYAAUiSwboPBIiuQRGRGY11gBxCSyg2WSAsorK8dnGk/htz3kcyy52rgjaNpCpaxeYSmUNgmophM4pMk+Cd7AEBtgUQjfiXkAMgAKRTQYokb2AiEjBRogNk00GaNuJC5aLz1wocy4DpARAqiCxUaYuThRCZ1syQDUFQI1/KzwDoEBkCYDOW2qAOA6DiKw1QDVkgCrLAWOlb8+J6maTAdp60jYAKnVuIryzW+AVTjRDzKkzAGINEPmDbQ0Ql8CISKGvYwkM4DywQGMoA4zm39+6qgFQmWtLYHXtAFM4kQHKUYqga1gCa2HTC0iua0xHA8UAKBApNUAl2UgIEw2quARGRJYlrqpLYJpgQB1sfwwFBiX7I6lRJoXin7PWwacuL4E5mwGqoxmi3RiMGgKgpKgQSBJQUWmyHNvYMAAKRKGx5vboMhI14q+53BI99JUm/54XEflXTUXQAJshBipL/U8Udp0tQKVNZ+UzF0oBXZT5uFomwle6GQDVkAEqLLOOwWge5ngXWLBGZalBbazLYAyAApFKZVkGi67MQbBa/DNlN+KOnERUB5MJMCgZoIjq1wdzHEZAsqn/2WZe/moTK4JV+wxQLUuXzjZBVCjH1RAAZReL+4t0MAbDVmMfisoAKFCZAyDJpg4oo4DLYERNlm1g4ygDpOVW+IBUZq750UVj64k8AMCY3skARGa/XG1ubFjrEpgyBqOOJogK5biaAqCi2pe/FC2iG/dWeAZAgcquFxC3whM1eUpgo9IAGgdvXA1kCSy3uAIPfbsTm47l+vtUfMO8BCbrorH9lPh6aKd4ROjEdvbMCvMSVG1F0JZdYC4WQVc6fs+oaweYwroVnktg5Es2O8GszRAZABE1Wbb1P5JU/foG0gxx8c5zWLTjLN5Zedjfp+Ib5iWwYlU4CsoMCAlSo2typCW4OFtmDoCcKYKuqwmiwlID5Dhwya5jB5iCS2DkHw7HYbAGiKjJUra3O6r/ARrMEtihDPE8DmU2ke365gxQhl78Hu+VEoUgtcoSXJwqMdfgGPXWWp+q3N0FVsMSWG2T4G1ZgjQGQORTyhJYMQeiEhFq3wFme3mAL4EdNAc+OcV65DaFjR3mDNDJUhFs9G/dDIA1u3K8yOZtuKZCaMsuMGeLoJ0LgGqaA6ZoYZMBaoy9gBgABSrbcRhRXAIjavIsTRDDHF/fAJbAZFnGYZvMz6HMwD1XrzFngA4XikxPv9QYANbsyun8cusOvpqWwSy7wLyTAXJ2CSw5WgRcZQYj8koaXy8gBkCByrYIWlkCYzdooqarooY5YIoGMA/sbH4ZSvRGy/dNYhnMnAE6XhIESQL6tlICIJv6Gks36Bp6AVl2gTkbANU+DNXSBLGOJTCtRm2pQW2MdUAMgAKVEgCV5iI+TBQ8MgNE1ITpa5gDprDsAgvcoOJwlYxPkwiAzBmgAjkMHeMjEBUSBKBKAFRXN2hXZ4HVsQ3e2V1gQOPeCs8AKFCFxFha2yeqxF8FReWVKNVz0CFRk6SvpQkiYNMIMXAzQErAo9Wo7L5v1MwZoEKEWZa/AOsSWF6JHkbl366mrfBebIQoxmA4twRme56NcSs8A6BAJUmWOqCwimyEBiszwbgMRtQk1VUE3QB2gSkF0Fd0jgcgaoAaY3GtHZsMUP/W1gAoKiQIkeZeQOXqOrJ3XmyEWFBmgMEoXvPmdRRBA417KzwDoEBmXgaTijMsM1kyuAxG1DTp66gBagCNEJUlsJHdE6GSxJtxViNv7yGbM0AiAGpmd52SXSmGeWmrziUwZxshKjVA1d8vlOxPpE4DrabmMRhVz7ExboVnABTIwhPEZ5txGKwDImqiKuqqAQrsWWAmk4zDWeI59GgRhdTmImBr1MtghjJIRhFwBIU3Q0oz+xoeJbtSYDJfXuMSmLIN3tUMUPVlqywnd4ApWjADRH7hcBxG4/5riYhqYMkANcxGiKcvlKLcYEKwRoXWzcPQMUE8j0a9Fd6c/amUVejSOhlSlQ7eSnYlt9KcsalxG7zSCdrVGiBHGSDndoBZz1EJgEob3XIlA6BAZjcOg80QiZq0OhshBvYS2EFzB+j2ceFQqyR0TBDPQ+kM3SiZ638KEYp+qc2qXa0EF9kGczBS0zb4Slc7QdecAXK2B5BC2QVWojciv9Tg3OM3EAyAAplNBije/MPKcRhETVSdNUCB3QfocJY4/06JIvPT0fz5UFbjDYBMpWISfIEchn42BdAKJQA6Xy62xtdcBO29URiubIEHAF2Q2hIsNbat8AyAApmjDFABM0BETVKFk52gDSWAyeSbc3KBkgHqYM78KEtghxvxTrCMrAwAQJEUjm7JUdWut2wxL1UCIC93gnZUBO1iBgiwZoEa21Z4BkCBzCYDlBilDERlAETUJFkaIdZRAwQEZB2QUuzcMV6cf2rzMASpJRRXVOJcI/3D7vTZcwAAkzYKwZrqb7dKgXFGhXk7ek1F0K7OAqtlGny2k4NQbTXWrfAMgAKZkgEqL0BiiPgLKbOwvNH+tUREtahrFIZGB0jmbc0BtgxWaTThWLY4J2UJLFijQptY806wRloHlJEpMkDaiOr1P4C1F1ARzDU7dRVBO7sEphRBmyoBo33djmUJLKLuHkAKazNEBkDkK7ooS8ozXsoHAJQbTCgsZzdooiZHCWpqKoKWpIAdiHoitxR6owkhQWrLcgoAm51gjTMAys/NBgBExMTVeEzLmFAUyUoAVEcNkNNLYDbb5avUAeUUubYLTJwjM0DkazbdoLVlWZYZMlncCUbUtBgrrcsgNW2DB6zZoQCbB6ZMgO+YEA6VyroVXAmADjbCACi3uAKmMlEEHRsbX+NxLWNCUIRa+gAZDYBsHiDr7BKYRgvA/Drb1AGZTK6NwVC0iGENEPmDXS8g8QPLbtBETYxtRqemDJDtdQG2BKYEOB0S7IM320LoxmbbyQuIlMS/gy6ieY3HiQyQUrNTIoJdW7Z1PM42QpQkh3VABWUGVJrMYzDCnA+AUswBUGPrBs0AKNA57AXErfBETYoSAKmDAU0ttRvKDrEAWwJTApxO1QIgEbAdziqCydS4ahu3nbyAaJgDUV10jceJDJBNYFO1DsjSzFCyDMh2ioNmiEr2JyokyGFRdk1aRIvzK6qoREFZ4+kFxAAo0DnoBs1miERNTF1NEBWWJbDACoCsGSD782/dPAzBGhXKDSacbmTLK1tPXkCUOQOEkOgaj0tpFopKaFABc0amagBk2wSxSifpWjlohuhqE0RFSLAazcNE8NWYlsEYAAU6uwyQ+KFlDRBRE1NXE0RFAM4Dq6g04kSO/Q4whVoloX2ceE4HG9FOsHKDEXvOFCBKyQCFVG+CqFAKjAstA1GrvA6u7gBTOBiImm1pguhCJqnKeTamQmgGQIHOYQaIS2BETUpFHT2AFAG4BHY8pwSVJhkRWg0SI6sX8SpBkdIpujHYe7YAeqMJMaq6l8CUAuPCmgaiuroDTOGgBkjJALmyA0zRGLfCMwAKdDYZoPgI8cuDRdBETYy+ji7QigBcAlOGnXZMjKg2DBSwLos1pgzQ1pNi95czS2CRuiBEhQRZd4JVWwIz/753dgeYwrIEZlsDJLbAu7oEBlgzQI2pEJoBUKCzZIC4BEbUZNXVBFERgH2AbLfAO9KpEfYC2nriArTQI0g2FwzXkgECzIXQSi+gahkgcwbH1SUwSxG0NWBxdQ6YLW9uhV93OAcPfLMDG47keHxfnmAAFOgiEsRnfRGSQsT2yKyiika3Y4KIaqF3sgg6AAMgywyweMfLd8pW+GPZJag0Bt4MM1fJsoztpy5Y638kde29m1BlJ1hNu8BcXgKrpQjarSUw79UALdx2Gj/vOoc/9mV6fF+eYAAU6LQRll9qzeULkCSg0iQjr1Tv5xMjIp+xFEHXUQMUgEtgVafAV9UiOgShwWrojSacyG34O4yO55Qgr0SPWI35ueii6ty91TImFMWyt5fAqhdBu9ME0fYcAc8nwpdUVOKPf0TgM7ZPC4/uy1MMgBoCcx1QUEmmpXkVt8ITNSHOboMPsEaI5QYjTuSKc6m6BV6hUknoEC+uawzLYEr9Tz+l+XMt9T8KuwxQjUtgTjZBVDjIAHm0BGYeYVJQZvCoF9Af+zJQZjAitXkoerWMcvt+vIEBUEPgoA6IARBRE+L0NvjA2gV2JKsYsgzEhAbVuuzSmGaCbTshAqBeseasTx31P4C5G3RNRdCWJTAXM0BVGiGKMRjmOWAuDEJVhGk1SG0ugqrlHixdLd5xDoDI/jgqivclBkANgWUnGLfCEzVJTjdCNC8zBcgssEM2IzBqe7NrTAHQ1pN5AIAu0eb5Xc5mgGoqgrY0QnQ1A2S/DT6/zACjG2MwbN3QPwUA8M3mU27dPruoAmsPiwGxY3v7d/kLYADUMCgBUHEmu0ETNUV6c2BQVw1QgC2BHaphBEZVHROVACgwMlfuOpdfhqPZ4rVvE+7cDjBA7LAqNC+BVZbV0AfI5RogcwBkrgFSlr+iQ10bg2Hrhv4toVFJ2HbyglvB6q+7z8EkA71SopEaW0dLBx9gANQQOBiIygwQURPidA1QYC2BHapjC7xCuf54TgkqKo31fl71oaLSiPu+3g4A6NMqGqFG87+BExmgSF0QTEHiNdCX5Ntf6aVGiJ40QVTER+gwrIsobnInC7R4x1kAwLjeyW6fgzcxAGoIHAxEZS8goibE2RogyxJYoAVAtWeAEiN1iNBpYDTJOJ4TGNkrV8iyjKcX78WOU/mI1Gnw5o29gfJ8caUTGSAA0EWIcRnGsgL7Kyy7wFztA6QEQPYZIHe2wNuaOLAVAODH7WdRbnA+WD2WXYxdZwqgVkm4phcDIHKWgwwQu0ETNSEVTnaCtu0DJPu3V1hJRaWlZ0xdAZAkSTZ1QIERvLni840n8d3WM1BJwLs39xXLO2X54konMkAAEBHZDAAgVSuCVnaBubkEZs4gWTJAbmyBt3Vphzi0iA5BQZkBS/aed/p2i3eeM98+1qMslDcxAGoIbMdhhHMJjKjJ0bs4Cwyy3fZnf1D6/8RFaBETVveuI0sA1MBGYmw4moPnf90HAHhsVGcM6RgnrnAxAxQZ0xwAoDFUHYbqYSNEcxG1J4NQbalVEm4aYC6G/vu0U7eRZRk/7RTLX4FQ/KxgANQQhJsDIEMpEnWisC63pAKGRtA1lYic4PQojDAA5t1Wfi6Edrb+R6Ec15B2gp3OK8V9X22H0SRjbO9k3HVpW+uVLmaAmjeLBQAEG0vss3fujsIIsh+FkVPk/hywqm7onwK1SsLmE3k44sQQ2x2n83EytxShwWqM6Jbg8eN7CwOghiA4VHQTBdDMmAuNSoIsW9d0iaiRU4KZuoqgJcl6jJ+3wh+qYwRGVQ1tJlipvhLTvtiGC6UG9GgRhZfG97Tf6u9iBiguVmSOVJDti9jdrQGyNEKsmgHyPABKjNJhaCdRDL3AiWLon8zFzyO6JiA0WOPx43sLA6CGwlwHpCrJQHwEl8GImgyjATCa/6/XlQECAmYn2KE6RmBU1cEcAJ3MK3WpuNYfZFnGowt3Y//5QsSGB+N/t/aDLkhtf5CLGaDkuBgYZPN92PYC8rgRopIB8k4RtOLmQWIZ7IftZ2r99zIYTfhlt6gV8vfoi6oYADUUtnVA7AVE1HTYZnLqqgECrEGSv5fAMlxbAosND0azsGDIMpxaVvGn91cfxW97ziNILeGDW/ohOdpBdsbFDFALm27QJYV51is8HoVhDoA8mAPmyJCO8UiK0uFCqQHL/smo8bh1h3PEbLTwYFzSPtYrj+0tDIAaCoe9gBgAETV6SiZHowPUTiwfBPt/IGpBmcGyU7VDHTvAFJLUMGaC/XkgE6/9cRAA8Ny13TEgtVn1gwxl1qUrZ3eB6YJQIonsXU5utvUKj4ehlsFkkpFbYh6D4aUMkFolYcKAujtDLzIvf13TMxkadWCFHIF1NlQzB72AGAARNQHONkFUWLbC+y+IOGwOYJKidIjUBTl9O2W57GCABkBHs4sx45udkGVg0qBWuHlQK8cHKstfksq5rJ2ZXi0CoLzcXOuFHjdCLMOFUr11DIaHu8Bs3dg/BSoJ2HQsD8eyqwfcxRWV+GOfyA4F2vIXwACo4VB2gnEeGFHT4mwTREUALIEpvXzq6v9TlZItOhyAvYAKyw246/OtKKqoxMDUZpg1ulvNB9suf6mcf5s1BonnX5DvIAByuxFimaX+JyY0CEFezMIkR4dYiqG/3VJ9S/wf/2Sg3GBCm9gwv09+d4QBUEPBDBBR01ThZA8gRQAsgbm6BV6h7AQ7GGC9gGRZxhM/7sGx7BIkR+nw/i19a5+n5WIBtOVxdJEAqtQAVboZACnHy0bkFIifhfpoQHiTuTP0wm1nqo0xUZofjumd7PfJ744wAGooHNQAZTEDRNT46Z3sAq0IgF1gzo7AqEoJmM7ml6G4otLr5+WuhdvO4Nfd56FRSXhvUt+6AwkXC6AV6hCRJSkvumC90N1dYDYB04VCsavMWwXQtoZ2ikNCpBZ5JXos35dpuTyrqBzrAmjyuyMMgBoKSwYoEwkRHIdB1GQ42wRRocwD82sA5N4SWHRosKXNx+EAqQM6ml2MZ3/+BwAwc0RH9GkVU/eN3MwAacPE8QZlHpjJaG2B4OouMHWwqEECUFAo7q8+MkAatQoT+lcvhv5l13mYZKB3gEx+d4QBUEOhBEDGCiRqReBTUGbgUFSixk7vahG0+c3GT0tgeSV6y5brDi4ugQHWoCkQ6oAqKo144JsdKNUbMbhdc9x9WTvnbuhmBigkQhxvKjP3Aaq0+f3u6i4wSbIETYWFIpisrxlcNw5IgSQB64/k4mSuqD1TRl+MC8DiZwUDoIZCowVCxHbLSH0OeqdEAwD+sEk5ElEj5GoRtO1AVD9Qlr9SmoW41fVXCYACYSfYq0sP4p9zhYgJDcKbE3pDpXKyjsXNDFBEtJgHpjUWo6jcYF3+AlzfBQZYls2KiutvCQwAWsaEWmagLdhyGkezi7HbPPn96p5J9fKY3sAAqCGxqQNK7yYyQrU1oCKiRsCyDd7J5SQ/7wKz1P84OQKjqkCZCbb6YBY+XnccAPDq9b0sm0+c4mYGSFkCi0ApzuaXWZsgqrUu7SazMGeASkrEz4Kng1Brc9MAczH01tNYuPUMAOCyAJr87ojfA6D33nsPqamp0Ol0GDRoEDZv3lzr8QsXLkTnzp2h0+nQo0cP/P7779WO2b9/P6699lpERUUhLCwMAwYMwKlTdc8rCXg2O8HSzQPlNh7NRUGZwY8nRUT1yt0MkJ9mgVkCICdHYFSl3M6fAVBWUTkeWbgLADA5rTWGd3VxgKebGSBoxS6wCJThTF6Z+00QFebblZWYl8DqKQMEAMO6xCMuQoucYj0+WXcMQGD2/rHl1wDo22+/xcyZMzFr1ixs374dvXr1Qnp6OrKyshwev2HDBkycOBFTp07Fjh07MHbsWIwdOxZ79+61HHP06FFccskl6Ny5M1avXo3du3fj6aefhk7n5g9QILHJALWNC0f7+HBUmmSsOuD49SKiRsDtRoh+WgLLUAqgXa//AWDpBp1ZWIGCUt//cWcyyXj4u13IKdajc2IEHr+qi+t34mYGSBl6HS6V4syFUvebICrMO8HKy0QGyFtzwBw+lFqFG/u3BAAYjDJCg9W40tXA0cf8GgC98cYbuOuuuzBlyhR07doVc+fORWhoKD799FOHx7/99tsYOXIkHn30UXTp0gUvvPAC+vbtizlz5liOefLJJ3HVVVfhlVdeQZ8+fdCuXTtce+21iI+P99XTqj82GSAAliwQl8GIGjGlo3MDaIQoyzIOZbm3BV4RoQtCC/NsLeW+fOmTdcex9nAOdEEqvDuxT/Uhp85wOwMkXrMIlOHMhTL3myAqzIGToVwEpfVVA6RQlsEAIL1bYkBNfnfEbwGQXq/Htm3bMHz4cOvJqFQYPnw4Nm7c6PA2GzdutDseANLT0y3Hm0wm/Pbbb+jYsSPS09MRHx+PQYMGYfHixbWeS0VFBQoLC+0+AlKEtRs0AEsd0OqD2QE/PZmI3ORqDZAfd4FlF1cgv9QAlQS0i3MvAwRYd4/5ehlsz5kCvLLsAADg6Wu6Oj3HrBp3M0DKEphUKgIgd5sgKsy308piDlizsPqrAQKAlGahGNU9ESoJmDiwhjEhAcRvAVBOTg6MRiMSEuxTZAkJCcjIcJzRyMjIqPX4rKwsFBcX46WXXsLIkSPxxx9/YNy4cbjuuuuwZs2aGs9l9uzZiIqKsnykpKR4+OzqiWUJTDzfHi2ikBylQ5nBiLWHc/x4YkRUb1yuAVL6APk+e6Isf6U2D3Mvc2KmdIQ+5MOO0CUVlXhgwQ4YjDJGdkvEzZ68gbubAdIpNUClOJ1XYt0F5mEApJP0aBYW7NUxGDV5c0Jv/PWvoRjYxsGQ2ADj9yJobzKZTACAMWPG4KGHHkLv3r3x2GOP4ZprrsHcuXNrvN3jjz+OgoICy8fp09VnmgSEKgGQJEkYYc4C/cFlMKLGqcLFTtC2S2CyXD/nVAMlY+NO/x9bSublkA97Ac36+R8czylBUpQOL43v4dnoBg8zQMGSEdkXCqy7wDysAQpBRb3uALOlC1KjZYyLTRv9xG8BUGxsLNRqNTIz7fvYZGZmIjEx0eFtEhMTaz0+NjYWGo0GXbt2tTumS5cute4C02q1iIyMtPsISMoSWHEGYA72RpjrgFbsz0Sl0eSvMyOi+qJ3cwnMVAlU+nZczsZjYohnp0TPfod2TRK333IiDxuP5tZxtOcWbj2N77edgUoC3prQG9GhHgQLhnLr7i1XM0DB4ZAhAi9TeRHKzMXLzu4CqzSasPdsAT7bcAIPfLMDv+4XIzV0MCA+ohFsBPIyvwVAwcHB6NevH1auXGm5zGQyYeXKlUhLS3N4m7S0NLvjAWD58uWW44ODgzFgwAAcPHjQ7phDhw6hdevWXn4GfhAeD0ASv9hKxS+FganNEBMahAulBmw5caH22xNRw1PhYhG07W4xH+4EO5VbihX7xR+o1/byrPldl6QIXNsrGZUmGfd8tQ2ncku9cYoOLd2bgcd+3AMAmH5FBwxq29yzO1SyP5LK+aBVoVJBUgqhpVLkK/WoNSyBGU0y/jqUjTf+OIibP9qEns/9gWveXYdZP/+Dn3edQ26FWIZsFQFMu6ytO8+mUfNrifbMmTMxefJk9O/fHwMHDsRbb72FkpISTJkyBQBw2223oUWLFpg9ezYAYMaMGRgyZAhef/11XH311ViwYAG2bt2KDz/80HKfjz76KCZMmIDLLrsMQ4cOxdKlS/HLL79g9erV/niK3qUOAsLigJIsUQgdHgeNWoVhXRLw/bYzWPZPBtLaefifl4gCi7Kby9lt8Cq1aIBnKBUBUFhs/Z2bjXkbjkOWgSEd49DezSaICkmS8Mr1PXEytwS7zhRg6mdb8OO9gxGhC/LS2QqrD2bh/m+2w2iScX2/lnhwWAfP71Sp/9FFude8UBsJVBQiAqUoLCxBEuBwCaygzID7vtqOdUfs6z8jtBr0aR2D/q1jMCw3FdgHTOwbB5g7NZOVXwOgCRMmIDs7G8888wwyMjLQu3dvLF261FLofOrUKahsfoAGDx6Mr7/+Gk899RSeeOIJdOjQAYsXL0b37t0tx4wbNw5z587F7Nmz8cADD6BTp0744YcfcMkll/j8+dWLiAQRABVblwLTuyXi+21nsHxfJmaN7urZ2jURue3jtcdQbjDivqHtvfP/sLICMJl74TibAQLEMpih1Gc7wQrLDfhui6idnHpJG6/cpy5IjQ9v649r56zD4axizFiwEx/d1h9qZ8dR1GHTsVz83xfbYDDKuLpnEl4e39P5URe1cbf+R6GLBApFBqi42Jz9q7IEdjK3BHfM34Kj2SUIDVZjZLdE9EuNQb/WMegQH2F9jVaZC5GV7fRkx++b9KdPn47p06c7vM5R1uaGG27ADTfcUOt93nHHHbjjjju8cXqBJyIJyNhj2QoPAJd2iEVosBpn88uw92wherSM8uMJEjVNm47l4j+/7QcA9EqJxqUdvPAXt20A42wGSDm2JNtnS2DfbTmNEr0RHeLDcWkH72WcEiJ1+Oi2/rhh7kb8eSALryw94F5jwip2ns7H1PlbUFFpwhWd4/Hmjb29Fli5vQNMYS6EDkcZykrM/342k+D/PpaLu7/chgulBiRF6fDJ5AHomlxDzZWydGY7U4wsGtUusCahSjNEQPylpAyiY1NEIt+TZRkvLTlg+X7On0e8c8fKVvagULG05Syt77pBVxpNmLf+BADgjkvaeD0D3bNlNF67oRcA4H9/HcPCrZ7t0t13rhC3ffI3SswT3t+f1BfBGi++FZaZazHdzQCZa4AipVKUl5n//cxDTX/Ydga3fPI3LpQa0KtlFH667+Kagx/AunRmqL8aqoaMAVBDYzMOwxaHoxL5z7J/MrDzdD5CgtQIUkv4+3getp7I8/yOXR2DobDMA6v/AOiPfZk4m1+GZmHBGFdPs59G90rGA1e0BwA8uWiv26/tkaxi3PrJ3ygsr0TfVtH46Lb+HvUrckhZAguJce/2Ous8sIpyEbiYNDq8uuwAHl64CwajjKt6JGLBtDTE1zWgVckAVTID5AgDoIbGQQYIAIZ2iodGJeFwVjGOZftnBhBRU1RpNOGVpWLn6V2XtsH4vmIe0nurvJAFcrUJosKH88A+MU9MnzSolfeDCRsPDu+IUd0ToTeacPeX28SsLBeczivFLR//jdwSPbolR2LelIEI09ZDFYiXlsAiUApjhXiOi/fm4b1VRwEA04e2x5yJfRES7MRrHcQMUG0YADU0NWSAokKDLDvA/tiXWfVWRFRPvtt6BsdyStAsLBh3XdYWdw9pB5UErDqYjb1nCzy7c1ebICp8NA9s5+l8bDt5AUFqCbdeVL+tRlQqCa/f2AtdkyKRU6zHXZ9vQ0lFpVO3zSgox80fb0JGYTk6xIfji6mDEBXi3R1lFt4ogoYoglYZReZmZ0YFgtQSXr+hFx5J7+R8sbYlAGIRtCMMgBqaGjJAACxdobkMRuQbpfpKvLXiEADg/ivaI0IXhNTYMIzulQwAeH+1h1kgpQbI1X4ylnlg9TtKQsn+jO6VXPdyjBeEBmvw0eT+iA3XYv/5Qsz8bidMJsfdrk0mGXkleuw7V4hJH2/C6bwytG4eii/vHFS/M7G8lAFqHqSHDmKGlzo4BF/deRHG92vp2n1pGADVxu+7wMhFSgaoOBMwGe0KI0d0TcDTi/dix6l8ZBaWI8EHv5CImrJ5608gq6gCKc1CcPMg6+yoey9vj592nsOSvRk4klXkfl+cCneXwJR5YPWXATqXX4bf94hMtLe2vjujRXQI/ndrP0z8cBOW/ZOJ6d9sR0xoMPJK9Mgt0eNCiR55JXpcKNXDNjZKitLhy6mD6v/3oqcZIHMAlBBcAblSBED3juiBOHdmazEDVCtmgBqasDjRYVQ2iW2uNhIidejTKhoAl8GI6lteiR5zV4u6jEdGdIJWY/1jpFNiBEZ0TYAsA++bj3GL3s0iaB/sAvts4wkYTTIuatsM3ZJ923qjX+sYzL6uBwDg9z0Z+OrvU1iyNwObj+fhcFYxckuswU+kToN+rWPw5Z2DkNLMBzOqPM0AmZfAujeX0CZavEXHRbt5X0r/IBZBO8QMUEOjUgPhCaIGqOi8dUnMLL1bInacyscf/2TU+5o8UVP23qojKKqoRNekSIzumVzt+vuGtscf+zLx085zeGh4R/fefN0uglaWwOonACrVV+Kbv8V8xamX+GfEwvh+LWGUZew+k49mocFoFhaMZuFaNA8TXzcPC0aMjyag2/FSBihSKkVkGIAiOD0LrBqlfxCLoB1iANQQRSSaA6DqtT7p3RLx0pID2Hg0FwWlBkSF1lOhH1ETdjqvFF9sPAkAeGxUZ4dFqaIZYizWHs7B//46iv+M7eH6A1XUPgi10miCxtEbfD3vAvth2xkUllcitXkohnWOr5fHcMaN/VNwY/8Uvz2+Q17KAKG8EIA5jRXkZubK3D+IjRAd4xJYQ1TDTjAAaBMbho4J4ag0yfjzIJfBiOrDm8sPQW804eL2zWvtfHzfUNG75rutZ5BV6MabUC0ZoO+2nkb3Z5fh9nmbq28J1yo1QN4PgEwmGZ+aGx9OubiNd8ZHNCYeZ4DM/3YVRdbAReOFDJDsuFi8KWMA1BDVshMMsDZF/OMfBkBE3rbvXCEW7TwLAPj3yM61dj4e1KYZ+reOgb7ShI/WHnP9wWpohPj136fwr+93o9xgwuqD2Uh/8y98vvGEdUdUPS6BrTqYheM5JYjUaXC9q7uSGjtDubXexsNdYKgoBCrNxcs1TIOvk+V2MmDUu3cfjRgDoIaolgwQYA2AVh/MRrnB6KuzImoSXll2ALIMXNMzCT1bRtd6rCRJuM/cwfirv0/hQomLb0IOMkBfbDyBJxbtAQDcNCAFA1JjUKI34pmf/sGN/9uIo9nFNktg3t8Fpmx9nziwVf00EmzIlOyPpHK9dYFCZy4oN5Ra2xh4HACBdUAOMABqiCwZIMcZnm7JkWgRHYIygxFrD+f48MSIGrcNR3Ow+mA2NCoJj4zo5NRtLu8Yh+4tIlGqN2Le+uOuPWCFfR+geeuP4+mf/gEguk7Pvq4Hvp2WhhfGdENYsBpbT17AqLfX4vu9+eJ2eu/2Adp3rhAbjuZCrZJw2+BUr953o6Bk5XVRgMrNt1etTeCkZG00bgZA6iBAZQ5SWQdUDQOghqiODJAkSRjRLQEAmyISeYssy3jZPPD05kGtkBrrXHdmSZJw3+UiCzR/wwkUlRucf1C9tRP0x2uP4blf9gEA7rm8HZ64qgskSYJKJeHWtFQse+gyDOkYB32lCf/blAUAqCzzbgD0qTmAG9k9ES2i3XxTbsz2LRafWw5w/z7UQdWLnt3NAAEciFoLBkANUbgIbmqqAQKAEV1FlmjF/kzoK02+OCsin3p35WGM/2ADzub7psnbkr0Z2HWmAKHBatx/RQeXbpveLRHt4sJQWF6JLzedcv6G5hqen/YX4j+/7QcgOk7/K71TtdqjljGhmD9lAN64sRfUOrEEZiwvxstLD3hlKTyrqBw/7zwHwLeNDxsMYyWwa4H4us8tnt2XtsrymScBEAei1ogBUEMUae45UpJdY6v7AakxaB4WjPxSA66dsw67Tuf77vyI6tnBjCK8ueIQtp28gBnf7EClsX6D/HKDEa8uUwaetkVchNal26tUEu41Z4E+WXfM+YDEnAH6eLNoevrQ8I54eET14EchSRKu69sSX947HACglQz4aPVBXPX2Wmw6luvSOdsymWS8v+oo9EYT+rSKRt9Wbk46b8yO/imy8iHNgI6jPLsvpRAaACS1yAq5S+khxG7Q1TAAaojCYoGYNgBk4PhfDg/RqFV47cZeiAkNwoGMIox7fz1e+HUfSvXODQ8kCmQvLz1g6fS79eQFvL3ycL09VkGZAbd+8jeO55QgNlwMPHXHtb2T0TImBDnFeizYXHcWSJZllJeIYaol0OGRER0xY7hzmafYZtaxCanhJhzLKcFNH27CYz/sRkGpC0twAHacuoBx76/H/A0nAIgAkBzY+aX43PNGQOPhrDGdTQDkbg+gqrfnElg1DIAaqg5Xis+Hl9d4yNBO8VgxcwjG9WkBkyx2b4x48y+sOZRd422IAt2mY7n480AW1CoJD1/ZEQAwZ9URrD/i/YL/rKJyTPjfRmw5cQEROg0+uKUfwt3c+RSkVuHuIe0AAB+sOYr3Vx/BFxtPYNGOM1i+LxMbj+Zi79kCnMgpQU5xBV5fdhCaSvGmdfvQHpjuyrKbOghQiyzVj3f1tswpW7DlNIa9sQa/7T4PuY6+MNlFFXhk4S6Me38Ddp0pQLhWg2eu6YpR3RNrvV3AWT4LeK0jkOvBSJK6lOYBB5eIr3tP8vz+bDNA7naBVrAZYo24h7Ghan8lsPlD4MhK0eCqhpR483At3pzQG2N6J+PJRXtx5kIZJn+6Gdf1aYGnrulav1ORibxMlmXMNhciTxyYgvuHdcDZ/DIs2HIaD367E0tmXIrYcNeWp2pyMrcEt36yGafyShEXocVnUwaia3Jk3TesxfX9WuKdlYeRWViBV5YerPVYLfR4RCeW9m4b0s31B9OGA6UViJTK8d9xPTC2dws8/uNuHM0uwX1fb8fwLvF4fkx3JFcpZjYYTfhswwm8veIwiioqLef9r5GdEB/RwAYsm4zA1nlARQGwbR4w4j/18zh7FoodW4k9gaSent+fbQbI3R1gCmaAasQMUEOVeon4C6/gFJBzqM7DL+8Ujz8eugx3XNwGkgT8uOMshr+xBj/tPFvnX4JEgeL3PRnYdTofocFqzBgmsj+zRndDh/hwZBdV4OHvdlmbAXpg37lCjP9gI07llaJVs1B8f3eax8EPAOiC1Pjwtv6YcnEqbujXEqO6J+LSDrHonRKN9vHhSIzUWTJMsUE2PYOCnNtxZqdKM8SBbZrh9xmX4oFhHRCklrBifxaufGMNPtsghpoCwNrD2Rj19lr857f9KKqoRM+WUfjx3sF47YZeDS/4AYCM3SL4AYA934uAqD7sMC9/eVr8rLAtgvakABrgQNRaMAPUUAWHAqkXi8K7w8uBuLp7koRpNXhmdFeM7pWEx3/cgwMZRZixYCcW7TiLp6/pinZxLg5cJPIhg9GEV5eJ7I9tIXJIsBpzbu6La+esw5pD2fh43TFMu6yd24+z+Xgepn62BUXlleicGIHP7xiI+Ejvvfn3TolG75ToWo8xmWQYc48B70E0NXSnp0xw9XEYWo0aM6/siGt6JuGxH3Zj+6l8zPr5HyzeeRZx4Vr8sU/0FmseFox/jeyEG/qleH/UxckNwHeTgcH3Axc/4N37rur4WuvXReeBE2uBtpd79zHO7xaBljoY6HGDd+5TG2X92tMlMGaAasQMUEPW3lwHdKTmOiBH+rSKwS/3X4JH0zshWKPC6oPZGPHmX3hy0R5kFfGvBApM32w+hRO5pQ4LkTslRmDWaLFM9MrSg9jp5q7Hlfszcesnf6OovBIDUmPw7f+leTX4cZZKJSGo0tzFOdjNP0yUDJCDeWAdEyLw/d2D8cKYbgjXarDjVD7+2JcJtUrC7YNT8ecjl2PCgFbeD34qioAf/w8oyQLWvGwdHFpfTpgDIKWmZvd33n+MnV+Jz51GAaHNaj/WWV5dAlP6APF3e1UMgBoypRD65AaXZ/4EqVW4b2h7LJlxKYZ3iYfRJOOrv0/h8ldX443lh1Bcwd1iFDiKKyrx9gqx02vGsA4OC5EnDkzB1T2SUGmScf8321HoSsNBiAnn077YhopKE4Z1jsfndwxCVIgH2489VcsgVKcot6vhd4PSQHH5zMswpncyhndJwO8PXIpnr+1Wf8/7j6fEsj0gnt+2+fXzOIDoy3Nyo/j6iqfE530/e3c7eKXeGlT1udV792tXBO1hAGQpgg6gDFDecWD+NUD+ab+eBgOghqx5eyC6tSi+O7G27uMdaBcXjo8nD8C30y5C75RolOqNeGflYQx5ZRU+23CCTRQbMlkGFk4BFkwSbwYN2Id/HUNuiR5tYsNw08BWDo+RJAn/va4HWsaE4HReGR7/cY9T9W2yLOPjtcfw8MJdMJpkXNe3Bebe2g8hwWpvPw3XVFi7QLvFMg+s9j+OkqJC8PZNffDx5P7olOjm/CpnHFlhDXj63iY+/z1XBBH14fxOMQpEFw30nwpEtRLfK7u1vOHQEqAsT3Tnb3eF9+5X58UASFkCC5QaoMLzwOdjxHvWbw/79VQYADVkkuTUdnhnDGrbHIvuHYwPJvVFm9gw5JboMevnf3Dlm2vwy65zLJRuiM7vAv75ETjwq7VFfwOUVVSOj82T1B9N74Qgdc2/tqJCgvDuxD7QqCT8tvs8Fmxx/BdmRaURaw5l4+nFezH4pT8tXZanXtIGr13fq9bH8Bm9/RwwlzkZAPlEWT7w0/3i60F3A1e9LoKGovPA3u/r5zGVHmmplwBqDdDTXJ/jzWUwpfi5102AyosBs20RtMbTGqAAaoRYmgd8MQ7IPyl62V37rl9PJwD+l5NHbOuAPAxSJEnCqB5J+OOhy/DC2O6IDdfiZG4p7v9mB8a8tx4f/XUMG47muNxIjfzk0FLr1+ve8vjnw1/eXnEYpXojeqdEO9WDpk+rGDyaLjYFPPvzPziUKQKJvBI9fth2Bvd8uQ19n1+OyZ9uxhebTuJ8QTlCgtR4fFRnPHV1F+/Xvbiron6XwHxq6eNA0TmgWTtg2CzRKHDQ/4nrNrxbPz+bSlY89VLxuceN4vOR5UCJ+12xLQrPi6wWAPT20u4vhbY+GiH6OQCqKAK+HA9k7wcikoHbfgIiEvx6StwF1tC1uVTsPsg/BeQcBuI6enyXQWoVbr2oNa7r0wIfrz2OD/86it1nCrD7TIHlmJRmIeiWFIXuLSLRLTkK3VpEer5NVpZFJmvNS+IvxilL/P4fpEGzDYAy94ieUR2G++983HA0u9iSxXl8VOcaR0BUddelbbH+aC7+OpSNaZ9vRXyEDltP5sF2h3x8hBbDuiTgyq7xGNwuFrogPy95VWUZhOpuEbSSASrxzvm46+ASYNfXACRg7AdiBysA9JsC/PUakLXP+z+bRgNwapP4uo05AIrvDCT1EpnRfYuAAXd69hi7FwCyCUi5CIht79l9VaWrj0aIfgyADOXANxOBc9vFqJDbFgMxrf13PmYMgBq64DCg9cXAsVXiLxsvBECKMK0GM4Z3wKSLWuG7raex50wB9p4rwOm8MsvHUptp83ERWozomoC7h7RDSjMX/mqRZeDYamDVi8CZLdbLd3wOXPao155Pk1KUAZzbIb7ucSOw5ztg3RsNLgB6delBGE0yhnWOx6C2zZ2+nUol4Y0be2HU22txIrcUJ3JFAWiXpEhc2SUew7smoHtyVOBkexxRAhd3M0CWXWDenQjvktI84JcZ4uvB04FWg6zXhUQDfScDm94DNrzj3Z/Ns9tF0W9IMyCui/XyHjeKAGj3d54FQLIM7DDv/urjhc7PVdlug/d4Ccz8u7gkS/yRLKnEcp2kFp9VGvPXKqC8ECjOAoozgOJM8XVRhs1lWUB4PDD0SWv5RV2MBuD7KSIjFxwB3PKDU21bfIEBUGPQfrg5AFoBpN3n9buPDddaBjkCQEGpAf+cL8C+c4XYe7YAe88V4lh2MbKLKvDV36ewYMtpjOmdjHsvb4/28XX88j65AfjzReDkOvG9JgRodZF4Pju/AS59pMYu11SLw3+Iz8l9gSufA/5ZBJxcD5z62/5NyEdK9ZXYc6YAO0/nY8/ZAsRFaDGscwIGtmmGYI3jlfhtJy9g6T8ZUEnAv0d1dvkxY8O1+Pi2/pi3/jj6tIrBsC7xaBnj4XKCLymDjt3NACl1JP5cAvv9UfFGGtsJGPpU9esvukcUQh9fA5zbCST39s7jnrCp/7HtodR9PLD8aeD032InUjM3p9qf2QLkHhbBRbdxnp9vVXaNED1dAjMXUR9ZAczp79l9AUDhWeCr64F2w4D0F4H4LjUfazIBP90HHPxdNO6d+A3Qoq/n5+AlDIAagw5XAn88CZxYD+hLrSnmehIVGoTB7WIxuF2s5bJSfSW2n8zHh2uP4a9D2fhx+1ks2nEWV3VPwr1D26FbcpT9nZzZBqz6j2jkCIhlvP53AJfMFOf/Wkcg76j4RZMysF6fT6N00Lz81XEkEJksijR3fAGsfwto9U29PrQsyzieU4Idp/Kx4/QF7DiVjwMZRZZuw4p5608gQqvBZR3jMKxLPIZ2ikeMeTSLLMt4aYkoTL6hXwo6JrhXCNwrJRpv3dTHsyfkL5Zt8J4WQftpCWzfT6LAWVID4z5wvJQTnQJ0v06Mktg4Bxj/sXceW2mA2OYy+8sjk4A2Q8QfWHsWAkP+5d797/hCfO46xv1/n9p4cwms7RAgvqsoODeZANkoOmLbflaotaLsILzqR7z4HBYH7P8J2DQXOLoS+GCVWMoc+oQY0m1LloGl/wZ2fyuyTDd+bl2ODBAMgBqD2I5ii2fBKZFm7Jju81MIDdbgkg6xuKRDLHafycecP4/gj32Z+G3Pefy25zyu6ByP+4a2Rz/dOWDlC2L7KCD+Y/S5FbjsESCqpfUOu1wr1th3fs0AyFWGcvELHrD+LFw8A/KOLyEd/B3nD+9AXlhbFJdXorhCfOgrTQgJViM0WI2QII3N1+JzaLAGQWoJ+WUGXCjR40KpAXklelwo1YvPJXrkleqRXVSB3WcKUFBWvVA+IVKLPikx6NEyCidzS/DngSzkFOstPyMqCejXOgZXdE5AmFaNLScuQBekwkNXem9Zt0Gp8LQGqOZGiPWuOBv49SHx9SUPAS361Xzs4PtFMLL3R2DYM0C04zYHTqusEBkewFoAbavnBPH/Y/d3Yond1QyzvgTYu0h87a3RF1VpdIAqCDAZPG+EGN0KuHdjzdfLsqhlMhnFEN26Xo+UAeKP1eXPAPt/AbZ+IsaMXPaIKGzXmGfxrXpRzKuEBIydC3Qa6dnzqAcMgBoDSRLr51s/FUXEfgiAbPVsGY0Pb+uPgxlFeG/VEfy6+xz+PJCFykPL8XHwGwiGASaocCTpGmxudSfygpNR+XcJ9MYDqDSaYDCa0KniYtyMBTDu+RHGK/+LYJ1rWa2SikoczS7GidxS6CtNMMkyTCYZJhkwyTJk2fq1SQZ0QSqEazXWD50GEdoghGnVCNdpoNUEWIFsbU6sAwylKAmOw+0/leB47goUlRvwpjQAV6k3Y8PnT+Nhwz31egpajQo9WkShT6to9GkVgz6topEUZf+L3GSSsetMPlbuz8KK/Zk4kFGELScuYMuJC5Zj7ri4DRKjAmAG1YHfgZXPi/EzV7/um8es50aI9UaWgd9mAqW5QHy3urMsSb1EVub4GpFZGPlfzx7/zFbR8yYs3nGtSZdrgF9DxBLWuR2uL8ns/0XUVcWkivrL+iBJIgtUmut5HyBnHkupB3JWs7bAhC/F75qlj4tRIMufFu9BI14ALpwA/npVHHv1a9YWBAGGAVBj0f5K8cOnbIcPgLqZTokReGdiHzx0ZUes/OVr3HpCBD9/GXvg2crJOHY8GTheDKD6MFcJMRiibY4W+lw89J/ZOBR7JbomRaKL+aNzUgSahwUjp1iPI1nFOJJdjKNZxTiaXYwjWcU4X+Ddpl/BahXCdVUDJI31MvP3CZE6XNE5Hs29NJHcWUaTjB2nLmD5vkx02fEJxgL4qbQHthTmW46ZK43GVerNuFa1AfPDb0aJLhkROnHuQWoVyvRGlBmMKNUbUaY3olRfiVK9ERVVmmFGhQShWVgwYkKVz8Hic1gwmoUGo3NSBDonRtZY26NQqSRzcBSDR9I74cyFUqw6kIUV+7Ow8Wgu4iK0uPty92d6eUVpHrDk36KIHBBbeHvcIOrU6punNUAOZoH5xN4fgP0/i+zuuLnWjEBtLn5ABEDbPxMBU0i0+49v2f5+iePfg9oIoPNV4jx3f+d6AKT0/uk9qX5/z2p9FAB5IvUSYNoaYNc3wMrngAvHgW9tsmLDnvF8t109YgDUWLS5TNTRXDgB5B71/rZMD7TJ34Q7zzwJSAYciL4MX8c8gS4aLXqqJWjUKgSpVQhSSwhSq6BRSwhWq1BYZsDfh67EdSULMFb6C3dkXIQDGUXAjrOW+9UFqVBuqLlTdWy4Fm1jwxCqVUMlSVBJoteRSoL5e0n88SNJKDcYUWJeDiour0RRRSVKKkQAAAB6owl5JWK5py4qCbiobXNc1SMJI7snIraegqEyvRHrjuRg+b4MrNyfhdwSPQAZ67SbAQnISByCF/p1Q9/WMYjUBSFCdyVMC1cg6Pga/NJvFzDqZqcex2iSUWYwwlBpQoROA009NQlsGROKW9NScWtaKsoNRkgS/Jt52/+rWMYpyRI7Z5p3AHIOAn/+B7j91/p/fI+3wftgCUyWgYpCoCRHvFkXZ1q7+172LyCpp3P3026YyBZl/QNsmyeWzdx13CYAqknPCSIA2vs9MOI/olGiMy6cMAdYEtBrovvn6AyltsjTXWD1TaUSO+G6jhE1hhveFRm4wQ+Ims4AxgCosdCGA63SxF9RR5YHTgB0ZKXo/2CsADpdjc43zMdcTbBzt815BJizAEM1e/DZ2NbYla/F/vOF2H++ECfzSlFuMEGSgJSYULSPDxcfceFoFx+G9nERiAr1fJ5RpdGEEr0RxeaAqKjcGiQVVxjsvi/RV2Lv2ULsOVuADUdzseFoLp75aS8GtWmOq3omYWS3RMsEc3fIsoz954uw7kg21h7OwebjeXbZmQidBpNSi9HyRA5kjQ4zp02rXhB/6UzxM7LtM1H/ULVw0QG1ShKzt7wRx1XqgT+fB5L7iB05NfBrT57SPLF7SelQHNsJGPu+KAJ9t694Azy22vtTxavyViNEbxRBy7LI6uz/FSjJBkpzrEGP0cEfBUm9xM+asyRJ1AItvlssg110r3OZo6oM5dZWGlULoG21uwIIbS6ey/HVYietM3Z+LT63vVwUcNenLteKn8WWA+r3cbxFGy5mrvW/Q/wRXlMGLoAwAGpMOlwp3twOLxfbS/3tyEpgwc2W4Ac3zBcdYJ0V2wFoOQDSmS0YUrEKQ4bdb7mqpKISGYXlaBEdUq9vlhq1ClEhKpeGQ57KLcXve8/j9z3nsftMATYey8XGYyIYGpjaDOndEtG6eSjiIrSIDRcfNS0XZRaWY93hHKw9nI11R3KRU1xhd32L6BBc2TUBV3YVW8qDNrwJnACkNkMc7wZsM0QEH+d2AH//D7jiSVdeDs/t/FL8hagOBlr0D4hmaHb2/SzqV0qyRdbn4hnAkMesO3H6TQE2/09kgdoMqd9f8B5ngMwZBEOpKHB1d1RDwVmR1VE2LjgSFAaENQdCY8XW8mHPiIJaV3QfL+qsis6Jolp3+uuc2Sx+34QnilmJNVEHAd2uA7Z8JJbBnAmATCbRmgOov+JnW0MeFYXFAR5EVBOZLD4aAAZAjUn74WLa8ol1PtkOX6ujf4rgp7LcveBH0Wui+Itu5zdA2nTLL4MwrQbt4tx8Y6hnrZqH4u4h7XD3kHY4nVeKJXvP47c9Gdh1Oh9/H8/D38fzqt0mOjQIseFaxIVrERuhRbhWje0n83Ew076JXUiQGhe1bYZLOsThsg6xaB8fbt8d+dAy8bmmQnhJEssL390mdmhc/ED9bON1xGQUwQ8gsgYrnweu/8Q3j12Xklzg90fE7DQAiOsssj5Vdy9dOhPY/rn4mTz8R/1uOLBkgNzdBm8zRFVfDOiiaj7WEZMJ2D4fWD5LLHOpgsQun4TuInMYFisCnrBY79SpaIKBi+4Wu4s2vAv0vtn1N3/L9vdL675tzwkiANr/i3ita8u0mUyikWjBKdGksPPVrp2Xuxpa8NPAMABqTOI6A5EtgcIzIgjqOMI/53H0T7HsVVkOdLrK/eAHED1Clj4uagMydovUuq+ZTMCvDwKmSuCat1x6LinNQjHtsnaYdlk7nLlQiiV7MrDpWC6yiiqQU1yB7KIKVJpk5JcakF9qwJEs+3oNSQJ6tIjCpR1icUn7OPRtHV1zXUxJLnB6s/i6tjfmzteIv45zj4ilsMHTnX4+HjnwK5B3TGQm9MViiemie4GWtWyRrm+VelF4u/olsawjqYFLHgSG/NvxEkxEIjDwLtG5+M8XxOYDVT3URMmy5xkgjVYUIpsqxTKYKwFQzhHRwVlpUNqivxhcmdDVvXNxVr/bgTWvimLzIyuc7zasqDr/qzYt+4uBnBeOi0Z9PW90fFxxFrD4Huvcr0H/F9iFyeQ0BkCNibIdftt88Z/VHwHQ0VVVgp/P3A9+ACAkBug0Skwz3/mNfwKg/T+LN0lAZDHGzXXrL7OWMaG467K2uOuytpbLTCYZBWUGSzCUbf5cUGZAp8QIXNwu1tIcsE6H/wAgA4k97HsqVaVSi6Wdn+8XzecG3uVevYUrZFkMZAXE8mzhWWDnVyJjOeV33/+la6wUDdrWvCTm6AFiZMLY9+veFXTJQ8DWeUDGHvGz0W2s98/PUArA3DjS3RogSRLBU3m+81vhjZXAxneBVbPFUlJQKHDF0+JN35vTzmuiiwL6TRY/l+vfdi0A0peKLfCAcw33JElkgda8JJbBHAVAR1YAi+4RhfAaHZD+X1HjQo0Cp8E3NrbT4X3t6Crgm5tE8NNxlOfBj6K3ebfSnu/EX+y+ZDICq2z6kuxeYP+9h1QqCTFhweiQEIHB7WMxpncL3HlpWzw8ohOu6ZnsfPADWIefdnSi4VjPCUBEkugOu/s7907eFSfWiUGIGh0wcJqYJaQJAU5tAA78Vv+PrzCZRMO99y8CfrpXBD/hCcCoV4H/W+PclujQZkDaveLrVf8VPyPepgQsksqzUQiWbtBOzAM7vwv4aCiw4lkR/LQdKhropd3rm+BHcdE9InN1Yq11np0zTm8SjQMjW4rMjjOUoOfonyLTo6jUA8ueFNPLS7JEJ+Vpq4EBU7ks1YgwAGps2g4Ra/V5x0Qlvi/IslhKsQ1+bvRS8AOILbJh8WLHia8Duz3fi63PumhgxIvisr9eAbZ/4dvzqEul3jpWxJkASKO1zo1b/1b9vInbWv+2+Nx7EhAeB0S1sD7+illiYGJ9kmUxHuR/l4nBjLmHRXbxyueBB3YCg6a5lgVLu0/8TOQcFF2Mvc12+cuTN1xnd4KteQX4cKhYZtZFi6ntty4Szf58LaqldYegkjV0hu32d2dfs+btRJ2XbBSBMSCW/z4ZLrJQADDgLuCuP2ufeUUNEgOgxkYbYW3SpqxZ1+b8bvFLpvCce49XXgB8fwfwywPWgucbP/PukopaY/1LTdmG6gtGA7B6tvj64gdErcylj4jvf33QGnAEglMbRaFqWJwYgOqMfreLJYfcI6I+xxFZBsouAFn7xbBKWXZ8XG0y9orAVVLZD+u95EFxvrlHxLJtfTm2GvjkSuCbCUDmHlGDdPnjwIzdYinQnc0CuihxW0D8jHg7gPO0CaJCuX1tS2DbPxdjC2Qj0HUsMH2LewXI3jTYvONz32LgYC27z2ydsCmAdkXPCeLz7m/FhPf/XSayYSExwE1fi07GrPlplBgANUbKuvnhWrIlpXnALw+K/+wrZgHv9hOFoK70DDmzFZh7qdg5o9IAw58T7dHro55EaTp2aJk4d1/Y+bUokAyLAwbdLS674inRCdhUCXw3Gcj8xzfnUhdl+atDuvNFudoIsRwFiH/7dW8CSx4Tz+uTdOCtnsCLicDLqWLJ6MMh1vb2rlB2fnW5VvzFbfv4lz9ufvzZIpj2lsoKYNcC4KMrgM/HiF1bmhARtDy4G7j8MfuBk+4Y9H/iZ+PCCVHP5E2ejsFQ1NUM8dwO4DdzUD/0SfHHS3i8Z4/pDYk9xK5PAPhpuv3ylCMVxcDZ7eJrZwqgbXW7ThS/n9sulkUNJeI+7tngu91e5BcMgBojpQ7oxFrAUGZ/nckIbPlYNHTbNg+ALNbLDaXiTejd/qLY2FRzh2WxJfRN4NN0IP+kGLZ3xzLxF3197IgBgMTu4peiySCWpZxV9fk7q7JCLAsA5gn15jcSSQLGvAe0vkRkXL66wf3smbfIsvWvZFe3ZQ+6WwQGWftE7cffH4i/uk9vEv+2leaRIsoOotWzRT2Ps/JPWxsKKhkTW30ni2G+pbniZ8pTBWfE9vo3ugKL/g84u030HBr4f8CMXWLJK7SZ548DiJ+JS81dj9e8IprweYtlEGpY7cfVRdlC7ygAKs0T7RCMFWLZWsluBoornhbdoUtzgJ/uqz37eGqTyGBFt3K9t1R4nGiMCIhA6Iqngdt+ajC9bMh9DIAao/guQGQL8eZ1Yr318lObxF/xvz0sljUSugO3/w48sENsVY9uJZqQLb4b+Ohyx290RZnAl9eJN0tTpfjr6e51YktpfetlLobe5cQyWKVeZDP+mwz8/i/Xl262zRftBCKSq+/60GiBm74Ub9yFZ4Gvb7QuWfhD7hGRqVIFAe2GunbbsFjgmjdEnVWvicDFDwIjXxYF7HcsE0HDk5nAY6dE/Y5sAn64U2y5d8am98XPSZvLHBcYqzUiKAGAje+LgMlVsizqP769VWSt1r4u3jQjW4g3s4f2AVe9AkQkuH7fdek3RTxO4VnvLuN5ugVeUdMSmMkI/HiXKAKPaSN2NtbXHy/uCtIB4z8C1Fqxw3FrLT2jTvwlPqfW0v25NqNeFv+WdywVzQd9WfRNfhNgP/HkFZIEtB8mvj6yHCjKAH6cJjI2GXvEX/OjXhVD7FIvFsd3Gwfct0UsY2kjxRr4/KuBBZOsxdRHVgBzLwaOrRJZg2vfBa7/1PUGa+7qcYNYaju3A8g6UPNxF06I5/r3B+INe/P/RNM/Z+lLgb9eE19f9oi1C7CtkBhg0kKxBJKxB1h4u9hC7A/K8lfqJe41zet9M3Drj+JN8MrnRDO6bmNFLVlMqvX5j3pFzMMqOi/6otQVVJbmieJ4wHH2R9FxpFhyMFaI3jrO0peIAcAfDAY+u0ZsSZeN4r5u/ELU+Fz2iPgLv74E6cRIEUAEXt4YOwFYA2pPm1TWtAS25hXx/1kTAkz4wrPho/UpoRsw/Fnx9bKngOzqg5MB2DdAdEfzdsDot4CUge7dnhokBkCNlbIMtvtbUd+z+1sAEtD3NuD+7WLXS9UBgEE6sYx1/3ag/1RRtHrgV+C9QWKp58vxYkRAfDexJbTvbb4tlAyPsz6vmrJA+38B5l4m1vN10SJrAQBLHxOjOZyx5SOx9TW6FdDn1pqPi0kFbv5WvIkcWQH8/rB7RcKeOmgOgDqNqt/H0YaLTKFaCxxeJrI7tdn6iainSOguMkw1kSRghDnw2f1t3VufTUaRbXmnjxhWmrVPjGLoPxW4d5MYVNr1WucHXHqqzy3iZ6Eky7VAuzbeygA52gV2eDmw5mXx9TVviqXlQDbobrElv7IM+PHO6q0wyguA8zvF17UNQCWqggFQY9X2cpEtKbsgfpm26A/ctVJkbeoagBkeJ5ZF7tkgxmuYDOYmewAG3CnuJ75zvT8Fh3qbi6F3f2e/dVtZ8vr2FqCiQAwQvHudqNfpdbPIBC2cAuQcrv3+ywutW2+HPFb3Vv4W/czjHCTxprz2tfrfUm6r7ILYAQYAHXzQ+DKxOzDS3Adp+Sxr4WlVhjIxawwQ2Z+6AuXkPkAP806/P552HEjKMnB4BTD3EtGluDgTiG4NjHwJmLlP/Mz6Y6uyOkj8rADiZ8cbxdxKwOJxEbQ5g6RklC6cEEuYkEXAqPx/CmQqldiWHxIjMtOrq/ThOrlR/P+OaVN7A1CiKhgANVa6SFH42byDCAKmLq8+16gu8V2AW34AJv0gtsdO+Aq4+nX/bgntOFJkdorOi6U4AMg7Dnw6Qix5AcDgB4ApS8S0ZkkSqe2WA0Vg9M1NImioyd9zgbI88bop22Pr0vlq8SYMiCGZr3UEFt0t+oqU5bv5RJ10ZKVY9onrLIZQ+kL/qWJHl8kgeuo4esPf9Y3IFka1Esurzhj2tMgunVhrnWmmyNgDfDEO+Gq8yPiExIjXfPpW0TjP30s4PW8UNWHl+WKnXN4xUZBdnC1eH0N59Y0FsiwCxZIc4MJJIHOf2Fl5bDWQuVcc43ENkM0SmKFM1EmV54vfBSNne3bfvhSZBIw295Ja95Z9baO729+pyeMojMZs5H+tf617osNw8REINFqgx/ViJ9vOb0S9zk/TRXATEgOMnQt0Gln9Njd9JRq95R4RmaBJ31dfIinNs27ZHvq4a0soF90t3mTWvy0KcHd9Iz4kNdAqTYwl6ZAOxHXy7rJhXcNP64MkiUziuZ0io/DLg6IWTHletkNP0+5zfip4dCsRzKx/C1j+tMg+lmQDq/4j+rNANu/omiZqe0JivP7U3KZSA0OfELVgG961Pv9qx2lEkCepxPKgXMtuS8Dz+jrbJbDfHxGNDkObAzd+Xv/jT7yt6xig9y3Azi/FDr971ovX57iHBdDUZEmy7I+ihcBWWFiIqKgoFBQUIDLSw14h5H1ntgEfXyHeRJQ3kJYDxZtwdErNtzu/WxRHG0pFXcGol+2vX/m8KGSN7yaWz9zZFWM0iN12h5cBh/4QnYJtRbcSW44HTxdfe8JYCbzWXmS0piwFWqd5dn+uOr1FvJ6yERj9jpjhBAD/LAYWThYBykP/uLaVu7wAeLu3yMJ1SDe3cigV13W7Dhj2jO8yXa4ymURG7PhfYtp9ZYXIkjlDEyIaMgaHiXqm4DAxomPkbNe3ddva+6M4p6BQ8TpKKtHhue3l7t+nP1UUiSXQCyfEkulVrwAvtwEgAw8fFMNqqUlz5f2bGSBqeFr0FcsNOeYdIRfPENud68o0JPUExv0P+O5WsdQV30V0QwbEUsWmueLrK550f0uwOkik4ttcCoz4j1ieO7xc7NQ6sU5sO978P9F995KHRIdpd5cUz2wWwU9IjKh58rWUAWLZasWzwJJ/ix00cZ2tYy8G3OV6HxtdlGhSuORfIogEgJRBYgxJih+eoytUKtFI0JbJJIIh5aOyQux2k2VzsGMOeupr27WyhKYEkVc81XCDH0DsirvuIxF47/kOYmCsLJasGfyQi1gDRA2PJInsTZvLgJu/E31knF1m6XotMPQp8fVvD1t7Ha17UyxJJPcRU+y9pVkbsePu1h+Bfx8HbvpGbNOuLBPFnO8NEsNA3UnEKtvf21/pux1PVQ2eIXZ4VZaJ5Z/Dy+2Hnrqj/x3i3za+m1iquWNZ4Ac/NVGpxO5KXaTYfBDVAmjWVmy7jkgUl9dnzxnbIupOVwEXP1R/j+UrKQOtrQeUOWys/yE3cAnMAS6BNXKyDPwwFdj7AxDSTMz7+XyM+Mv8lh9E7Ul9P/4/P4rdToVnxWXthomgLraD8/fz3iAg+wAw/hNRF+UvxdmiP1RxpqhvMVaI3YJXv+6/cyKhOAt4s7tYbr1zhf+Lxb3FaBBZoLPbxPfXf2odoEpNmivv3wyAHGAA1AQYyoB5o0TPGUkt6lhaDQam/O673kb6ElFztOFdsTyiCgLS7hV/3VZtgGcyiiW/s9tFhuXsNuu5/+uY/9/Yjq0GPh8LQBZ1JvdvE5kO8r/iLPHz1NgGeuYeFbMMTUbgwT312/CSGgwGQB5iANREFJ4TO8OKM8T3t/8uOmP7Wu5R0ahR6bUUkSSKfYNCRMBzdrto9OZonlPPm4Dr/ufT063Rn/8RW8B73ChGGBDVt9yjoq4qoau/z4QCBAMgDzEAakLObgO+uE4UhlYtYPW1g0tFIHThuOPrg8KA5N6iTqlFP1EMHt3at924ayPLYup6Qnexo4mIyMcYAHmIAVATY6z0XxFxVYZyYOO7wNb5omi2RV8gua8IeOI6cUgjEVEtGAB5iAEQERFRw+PK+ze3wRMREVGTwwCIiIiImhwGQERERNTkMAAiIiKiJocBEBERETU5AREAvffee0hNTYVOp8OgQYOwefPmWo9fuHAhOnfuDJ1Ohx49euD333+3u/7222+HJEl2HyNHjqzPp0BEREQNiN8DoG+//RYzZ87ErFmzsH37dvTq1Qvp6enIyspyePyGDRswceJETJ06FTt27MDYsWMxduxY7N271+64kSNH4vz585aPb775xhdPh4iIiBoAv/cBGjRoEAYMGIA5c+YAAEwmE1JSUnD//ffjscceq3b8hAkTUFJSgl9//dVy2UUXXYTevXtj7ty5AEQGKD8/H4sXL3brnNgHiIiIqOFpMH2A9Ho9tm3bhuHDrdO3VSoVhg8fjo0bNzq8zcaNG+2OB4D09PRqx69evRrx8fHo1KkT7rnnHuTm5nr/CRAREVGD5Nf+/zk5OTAajUhISLC7PCEhAQcOHHB4m4yMDIfHZ2RkWL4fOXIkrrvuOrRp0wZHjx7FE088gVGjRmHjxo1Qq6uPEqioqEBFRYXl+8LCQk+eFhEREQW4ABmA5F033XST5esePXqgZ8+eaNeuHVavXo1hw4ZVO3727Nl47rnnfHmKRERE5Ed+XQKLjY2FWq1GZmam3eWZmZlITEx0eJvExESXjgeAtm3bIjY2FkeOHHF4/eOPP46CggLLx+nTp118JkRERNSQ+DUACg4ORr9+/bBy5UrLZSaTCStXrkRaWprD26SlpdkdDwDLly+v8XgAOHPmDHJzc5GUlOTweq1Wi8jISLsPIiIiarz8vg1+5syZ+Oijj/DZZ59h//79uOeee1BSUoIpU6YAAG677TY8/vjjluNnzJiBpUuX4vXXX8eBAwfw7LPPYuvWrZg+fToAoLi4GI8++ig2bdqEEydOYOXKlRgzZgzat2+P9PR0vzxHIiIiCix+rwGaMGECsrOz8cwzzyAjIwO9e/fG0qVLLYXOp06dgkpljdMGDx6Mr7/+Gk899RSeeOIJdOjQAYsXL0b37t0BAGq1Grt378Znn32G/Px8JCcnY8SIEXjhhReg1WqdOielMwCLoYmIiBoO5X3bmQ4/fu8DFIjOnDmDlJQUf58GERERueH06dNo2bJlrccwAHLAZDLh3LlziIiIgCRJXr3vwsJCpKSk4PTp06w18gG+3r7F19u3+Hr7Fl9v33Ln9ZZlGUVFRUhOTrZbPXLE70tggUilUtUZOXqKxda+xdfbt/h6+xZfb9/i6+1brr7eUVFRTh3n9yJoIiIiIl9jAERERERNDgMgH9NqtZg1a5bTO9LIM3y9fYuvt2/x9fYtvt6+Vd+vN4ugiYiIqMlhBoiIiIiaHAZARERE1OQwACIiIqImhwEQERERNTkMgHzovffeQ2pqKnQ6HQYNGoTNmzf7+5Qahb/++gujR49GcnIyJEnC4sWL7a6XZRnPPPMMkpKSEBISguHDh+Pw4cP+OdlGYPbs2RgwYAAiIiIQHx+PsWPH4uDBg3bHlJeX47777kPz5s0RHh6O8ePHIzMz009n3LB98MEH6Nmzp6UZXFpaGpYsWWK5nq91/XrppZcgSRIefPBBy2V8zb3n2WefhSRJdh+dO3e2XF+frzUDIB/59ttvMXPmTMyaNQvbt29Hr169kJ6ejqysLH+fWoNXUlKCXr164b333nN4/SuvvIJ33nkHc+fOxd9//42wsDCkp6ejvLzcx2faOKxZswb33XcfNm3ahOXLl8NgMGDEiBEoKSmxHPPQQw/hl19+wcKFC7FmzRqcO3cO1113nR/PuuFq2bIlXnrpJWzbtg1bt27FFVdcgTFjxuCff/4BwNe6Pm3ZsgX/+9//0LNnT7vL+Zp7V7du3XD+/HnLx7p16yzX1etrLZNPDBw4UL7vvvss3xuNRjk5OVmePXu2H8+q8QEgL1q0yPK9yWSSExMT5VdffdVyWX5+vqzVauVvvvnGD2fY+GRlZckA5DVr1siyLF7foKAgeeHChZZj9u/fLwOQN27c6K/TbFRiYmLkjz/+mK91PSoqKpI7dOggL1++XB4yZIg8Y8YMWZb58+1ts2bNknv16uXwuvp+rZkB8gG9Xo9t27Zh+PDhlstUKhWGDx+OjRs3+vHMGr/jx48jIyPD7rWPiorCoEGD+Np7SUFBAQCgWbNmAIBt27bBYDDYveadO3dGq1at+Jp7yGg0YsGCBSgpKUFaWhpf63p033334eqrr7Z7bQH+fNeHw4cPIzk5GW3btsWkSZNw6tQpAPX/WnMYqg/k5OTAaDQiISHB7vKEhAQcOHDAT2fVNGRkZACAw9deuY7cZzKZ8OCDD+Liiy9G9+7dAYjXPDg4GNHR0XbH8jV33549e5CWloby8nKEh4dj0aJF6Nq1K3bu3MnXuh4sWLAA27dvx5YtW6pdx59v7xo0aBDmz5+PTp064fz583juuedw6aWXYu/evfX+WjMAIiK33Xfffdi7d6/dmj15X6dOnbBz504UFBTg+++/x+TJk7FmzRp/n1ajdPr0acyYMQPLly+HTqfz9+k0eqNGjbJ83bNnTwwaNAitW7fGd999h5CQkHp9bC6B+UBsbCzUanW1yvXMzEwkJib66ayaBuX15WvvfdOnT8evv/6KVatWoWXLlpbLExMTodfrkZ+fb3c8X3P3BQcHo3379ujXrx9mz56NXr164e233+ZrXQ+2bduGrKws9O3bFxqNBhqNBmvWrME777wDjUaDhIQEvub1KDo6Gh07dsSRI0fq/eebAZAPBAcHo1+/fli5cqXlMpPJhJUrVyItLc2PZ9b4tWnTBomJiXavfWFhIf7++2++9m6SZRnTp0/HokWL8Oeff6JNmzZ21/fr1w9BQUF2r/nBgwdx6tQpvuZeYjKZUFFRwde6HgwbNgx79uzBzp07LR/9+/fHpEmTLF/zNa8/xcXFOHr0KJKSkur/59vjMmpyyoIFC2StVivPnz9f3rdvnzxt2jQ5OjpazsjI8PepNXhFRUXyjh075B07dsgA5DfeeEPesWOHfPLkSVmWZfmll16So6Oj5Z9++knevXu3PGbMGLlNmzZyWVmZn8+8YbrnnnvkqKgoefXq1fL58+ctH6WlpZZj7r77brlVq1byn3/+KW/dulVOS0uT09LS/HjWDddjjz0mr1mzRj5+/Li8e/du+bHHHpMlSZL/+OMPWZb5WvuC7S4wWeZr7k0PP/ywvHr1avn48ePy+vXr5eHDh8uxsbFyVlaWLMv1+1ozAPKhd999V27VqpUcHBwsDxw4UN60aZO/T6lRWLVqlQyg2sfkyZNlWRZb4Z9++mk5ISFB1mq18rBhw+SDBw/696QbMEevNQB53rx5lmPKysrke++9V46JiZFDQ0PlcePGyefPn/ffSTdgd9xxh9y6dWs5ODhYjouLk4cNG2YJfmSZr7UvVA2A+Jp7z4QJE+SkpCQ5ODhYbtGihTxhwgT5yJEjluvr87WWZFmWPc8jERERETUcrAEiIiKiJocBEBERETU5DICIiIioyWEARERERE0OAyAiIiJqchgAERERUZPDAIiIiIiaHAZAREROkCQJixcv9vdpEJGXMAAiooB3++23Q5Kkah8jR47096kRUQOl8fcJEBE5Y+TIkZg3b57dZVqt1k9nQ0QNHTNARNQgaLVaJCYm2n3ExMQAEMtTH3zwAUaNGoWQkBC0bdsW33//vd3t9+zZgyuuuAIhISFo3rw5pk2bhuLiYrtjPv30U3Tr1g1arRZJSUmYPn263fU5OTkYN24cQkND0aFDB/z888/1+6SJqN4wACKiRuHpp5/G+PHjsWvXLkyaNAk33XQT9u/fDwAoKSlBeno6YmJisGXLFixcuBArVqywC3A++OAD3HfffZg2bRr27NmDn3/+Ge3bt7d7jOeeew433ngjdu/ejauuugqTJk1CXl6eT58nEXmJV0aqEhHVo8mTJ8tqtVoOCwuz+3jxxRdlWRYT6u+++2672wwaNEi+5557ZFmW5Q8//FCOiYmRi4uLLdf/9ttvskqlkjMyMmRZluXk5GT5ySefrPEcAMhPPfWU5fvi4mIZgLxkyRKvPU8i8h3WABFRgzB06FB88MEHdpc1a9bM8nVaWprddWlpadi5cycAYP/+/ejVqxfCwsIs11988cUwmUw4ePAgJEnCuXPnMGzYsFrPoWfPnpavw8LCEBkZiaysLHefEhH5EQMgImoQwsLCqi1JeUtISIhTxwUFBdl9L0kSTCZTfZwSEdUz1gARUaOwadOmat936dIFANClSxfs2rULJSUlluvXr18PlUqFTp06ISIiAqmpqVi5cqVPz5mI/IcZICJqECoqKpCRkWF3mUajQWxsLABg4cKF6N+/Py655BJ89dVX2Lx5Mz755BMAwKRJkzBr1ixMnjwZzz77LLKzs3H//ffj1ltvRUJCAgDg2Wefxd133434+HiMGjUKRUVFWL9+Pe6//37fPlEi8gkGQETUICxduhRJSUl2l3Xq1AkHDhwAIHZoLViwAPfeey+SkpLwzTffoGvXrgCA0NBQLFu2DDNmzMCAAQMQGhqK8ePH44033rDc1+TJk1FeXo4333wTjzzyCGJjY3H99df77gkSkU9JsizL/j4JIiJPSJKERYsWYezYsf4+FSJqIFgDRERERE0OAyAiIiJqclgDREQNHlfyichVzAARERFRk8MAiIiIiJocBkBERETU5DAAIiIioiaHARARERE1OQyAiIiIqMlhAERERERNDgMgIiIianIYABEREVGT8/+NENl6fDEWNAAAAABJRU5ErkJggg=="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = model.predict(np.expand_dims(X_test, axis=2))","metadata":{"execution":{"iopub.status.busy":"2023-11-15T14:01:13.099829Z","iopub.execute_input":"2023-11-15T14:01:13.100081Z","iopub.status.idle":"2023-11-15T14:01:55.557016Z","shell.execute_reply.started":"2023-11-15T14:01:13.100059Z","shell.execute_reply":"2023-11-15T14:01:55.555972Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"12160/12160 [==============================] - 34s 3ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred_classes = np.argmax(y_pred, axis=1)\n\n# Inverse the 'attacks' dictionary to map back numbers to names\ninverse_attacks = {v: k for k, v in attacks.items()}\n\n# Generate the classification report with attack names instead of numbers\nclass_report = classification_report(y_test, y_pred_classes, target_names=[inverse_attacks[i] for i in range(len(inverse_attacks))])\n\nprint(class_report)","metadata":{"execution":{"iopub.status.busy":"2023-11-15T14:01:55.558665Z","iopub.execute_input":"2023-11-15T14:01:55.559052Z","iopub.status.idle":"2023-11-15T14:01:56.092047Z","shell.execute_reply.started":"2023-11-15T14:01:55.559013Z","shell.execute_reply":"2023-11-15T14:01:56.091137Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"name":"stdout","text":"              precision    recall  f1-score   support\n\n      Normal       1.00      1.00      1.00    279968\n        DDoS       0.98      0.97      0.98     57614\n    Scanning       0.94      0.94      0.94     14163\n   Injection       0.72      0.95      0.82     20415\n        MITM       1.00      1.00      1.00        67\n     Malware       0.95      0.62      0.75     16863\n\n    accuracy                           0.97    389090\n   macro avg       0.93      0.91      0.91    389090\nweighted avg       0.98      0.97      0.97    389090\n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Compute the confusion matrix\nconf_mat = confusion_matrix(y_test, y_pred_classes)\n\n# Convert the 'attacks' dictionary to a list of class names ordered by the class number\nclass_names_ordered = [attack for attack, number in sorted(attacks.items(), key=lambda item: item[1])]\n\n# Plot the heatmap using seaborn\nplt.figure(figsize=(15, 10))\nsns.heatmap(conf_mat, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=class_names_ordered, yticklabels=class_names_ordered)\nplt.xlabel('Predicted Labels')\nplt.ylabel('True Labels')\nplt.title('Confusion Matrix')\nplt.show()\nplt.savefig('con_mat.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-15T14:01:56.093386Z","iopub.execute_input":"2023-11-15T14:01:56.093773Z","iopub.status.idle":"2023-11-15T14:01:56.522102Z","shell.execute_reply.started":"2023-11-15T14:01:56.093739Z","shell.execute_reply":"2023-11-15T14:01:56.521189Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 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size 640x480 with 0 Axes>"},"metadata":{}}]},{"cell_type":"code","source":"# Compute the confusion matrix\nconf_mat = confusion_matrix(y_test, y_pred_classes)\n\n# Normalize the confusion matrix by dividing each value by the sum of its row (i.e., the number of true instances for each label)\nconf_mat_normalized = conf_mat.astype('float') / conf_mat.sum(axis=1)[:, np.newaxis]\n\n# Convert the 'attacks' dictionary to a list of class names ordered by the class number\nclass_names_ordered = [attack for attack, number in sorted(attacks.items(), key=lambda item: item[1])]\n\n# Plot the heatmap using seaborn\nplt.figure(figsize=(15, 10))\nsns.heatmap(conf_mat_normalized, annot=True, fmt=\".2%\", cmap=\"Blues\", xticklabels=class_names_ordered, yticklabels=class_names_ordered)\nplt.xlabel('Predicted Labels')\nplt.ylabel('True Labels')\nplt.title('Normalized Confusion Matrix as Percentages')\nplt.show()\nplt.savefig('con_per.jpg')","metadata":{"execution":{"iopub.status.busy":"2023-11-15T14:01:56.523277Z","iopub.execute_input":"2023-11-15T14:01:56.523595Z","iopub.status.idle":"2023-11-15T14:01:57.015152Z","shell.execute_reply.started":"2023-11-15T14:01:56.523569Z","shell.execute_reply":"2023-11-15T14:01:57.014249Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype):\n/opt/conda/lib/python3.10/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n  if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 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size 640x480 with 0 Axes>"},"metadata":{}}]}]}